ReservoirSnap

Fingerprint Verify

Seamlessly integrate fingerprint authentication for quick and secure user verification. This feature ensures that only authorized experts gain access to ReservoirSnap’s sensitive, real-time insights by utilizing biometric data, reducing the risk of unauthorized access.

Requirements

Fingerprint Enrollment Setup

"As a field operator, I want to enroll my fingerprint quickly so that I can set up secure access to ReservoirSnap without delay."

Description

Implement a module that allows new users to enroll their fingerprint data securely with integration into existing user profiles. This module should guide users through interactive enrollment steps, provide feedback on data quality, and store fingerprint templates securely, ensuring a reliable foundation for biometric authentication.

Acceptance Criteria

New User Fingerprint Enrollment

Given a new user initiates fingerprint enrollment, when the user follows the guided enrollment steps, then the system must display clear instructions and real-time feedback on scan quality.

Data Quality Feedback during Enrollment

Given that a fingerprint scan is in progress, when the system analyzes the scan, then it must provide immediate, clear feedback indicating whether the data quality meets the required standard or if a re-scan is needed.

Secure Fingerprint Data Storage

Given a successful fingerprint capture, when the template is created, then the system must encrypt and securely store the fingerprint data within the associated user profile.

Enrollment Integration with User Profiles

Given the completion of the fingerprint enrollment process, when the fingerprint template is generated, then the system must accurately link the template to the correct user profile for future authentication.

Handling Enrollment Errors

Given an error occurs during the fingerprint scanning process, when an error is detected, then the system must display an appropriate error message and offer the option to retry enrollment.

Real-Time Fingerprint Verification

"As an engineer, I want my fingerprint to be verified immediately upon scanning so that I can access sensitive operational insights without additional delays."

Description

Develop a feature that enables quick, real-time fingerprint scanning and matching against stored templates to verify users before granting access. The system must ensure that the verification process is fast, reliable, and seamlessly integrated with core authentication workflows, minimizing operational delays.

Acceptance Criteria

Fingerprint Verification - Successful Match

Given a valid registered fingerprint is presented, when the fingerprint is scanned, then the system must match it against stored templates and grant access if they match.

Fingerprint Verification - Real-Time Processing

Given a user initiates a fingerprint scan, when the scan is processed, then the system should complete the verification in under 2 seconds to ensure real-time access.

Fingerprint Verification - Reattempt on Failure

Given an initial fingerprint verification fails, when the user reattempts, then the system should allow up to 3 attempts before locking the account or escalating the security protocol.

Fingerprint Verification - Integration with Authentication Workflow

Given that a user has successfully verified their fingerprint, when the system grants access, then it must seamlessly integrate the verification with the core ReservoirSnap authentication workflow and update the session accordingly.

Fingerprint Verification - Security and Privacy

Given fingerprint data is highly sensitive, when processing and storage occur, then the system must encrypt all biometric data in transit and at rest, complying with industry security standards.

Fallback Authentication Mechanism

"As a field operator, I want an alternative login method available so that I can still access ReservoirSnap when fingerprint scanning does not work."

Description

Incorporate a backup authentication method that activates if fingerprint verification fails, offering alternative options such as PIN or password entry. This ensures continuous access for users, maintains robust security, and provides a reliable pathway in situations where biometric data is unavailable or erroneous.

Acceptance Criteria

Fallback Triggered Authentication

Given that the fingerprint verification fails during login, when the system detects the failure, then it should automatically display the fallback authentication options (PIN or password) to the user.

Fallback PIN/Password Submission

Given that the fallback authentication option is displayed, when the user enters a valid PIN or password, then the system should authenticate the user and grant access to ReservoirSnap’s features.

Alternate Path Verification on Biometric Unavailability

Given that the biometric data is unavailable or erroneous, when the user attempts login, then the system must prompt the fallback authentication method ensuring continuity of service without compromising security.

Lockout Mechanism after Multiple Fallback Failures

Given that the user has failed the fallback authentication multiple times, when the threshold is reached, then the system should lock the user out and prompt a secure error message with instructions for contacting support.

Secure Biometric Data Storage

"As a compliance officer, I want fingerprint data to be stored securely so that user privacy is protected and the system complies with regulatory standards."

Description

Integrate end-to-end encryption for storing fingerprint data securely within ReservoirSnap. The system should utilize industry-standard encryption protocols to protect biometric information, complying with data protection regulations and ensuring that all stored data remains confidential and tamper-proof.

Acceptance Criteria

Fingerprint Enrollment Verification

Given a new fingerprint is scanned, when the data is captured, then the system must immediately encrypt the biometric data using industry-standard encryption protocols before storage.

Real-time Data Transmission Encryption

Given a fingerprint authentication attempt, when data is transmitted between the client and server, then the system must use end-to-end encryption with industry-standard protocols.

Unauthorized Access Prevention

Given a user without proper clearance, when attempting to access biometric data, then the system must deny access and log the event as a security incident.

Tamper-proof Storage Validation

Given stored fingerprint data, when an integrity check is performed, then any alterations must be detected and trigger an immediate security alert.

Regulatory Compliance Check

Given the requirement for secure biometric storage, when the system undergoes auditing, then it must demonstrate compliance with all data protection regulations for encrypted biometric data.

Token Guardian

Deploy advanced one-time token generation methods to reinforce security during logins. The Token Guardian creates dynamic passcodes that serve as a vital second layer of defense, ensuring that each access attempt is uniquely verified and closely monitored.

Requirements

Dynamic One-Time Token Generation

"As a field operator, I want a secure, unique passcode for every login attempt so that I can ensure my account is not compromised."

Description

Integrate dynamic one-time token generation that resets after each login attempt, ensuring each access has a unique code that prevents replay attacks and reinforces account security. This approach seamlessly integrates with the existing login system to provide an additional layer of verification.

Acceptance Criteria

Token Generation at Login Initiation

Given a user initiates a login, when the login process starts, then the system must generate a new one-time token and display it as part of the authentication process.

Unique Token for Each Login Attempt

Given a user makes consecutive login attempts, when each authentication request is processed, then the system must generate a unique one-time token for each attempt that cannot be reused.

Token Expiry After Use

Given a user successfully logs in using the one-time token, when the authentication process concludes, then the system must immediately invalidate and expire the token to prevent reuse.

Integration with Existing Login Workflow

Given an active login attempt in the existing system, when a one-time token is generated, then the token mechanism must integrate seamlessly without disrupting the current authentication flow.

Audit and Monitoring of Token Generation

Given a security requirement for logging, when a one-time token is generated, then the system must create an audit log entry capturing the timestamp, user identifier, and token generation event.

Automated Token Expiry and Refresh

"As a security engineer, I want tokens to expire quickly so that any intercepted token cannot be misused over time."

Description

Implement automatic token expiry and refresh mechanism that invalidates tokens upon use or after a short validity period, reducing the risk of token reuse or interception, and maintaining system integrity with robust security standards.

Acceptance Criteria

Token Expiry After Use

Given a user login request with a one-time token, when the token is successfully used, then the system should immediately mark it as invalid for further use.

Token Auto-Refresh on Expiry

Given a valid token that is nearing its short validity period, when the expiry time is reached, then the system should automatically generate and issue a refreshed token before the current token becomes invalid.

Handling Invalid or Expired Tokens

Given a login attempt with an expired or previously used token, when the system identifies the token as invalid, then the system should reject the login and prompt the user to request a new token through the secure refresh mechanism.

Token Refresh Performance Under Load

Given high-volume simultaneous login attempts requiring token refresh, when tokens are refreshed, then the system should maintain response times within acceptable performance limits (e.g., under 1 second per token issuance) without failures.

Real-time Token Activity Monitoring

"As a system administrator, I want to monitor token activities in real time so that I can quickly detect and mitigate potential security issues."

Description

Enable real-time monitoring of token generation and usage events by logging each token's lifecycle, detecting anomalies, and triggering alerts on suspicious activities to support proactive security management.

Acceptance Criteria

Token Lifecycle Logging

Given an access login attempt, when a token is generated, then a log entry with timestamp, token ID, and lifecycle stage is created.

Anomaly Detection in Token Usage

Given token usage events, when an anomaly such as repeated failed validations or irregular access patterns occurs, then the system detects the anomaly and triggers an alert.

Real-time Alerting and Incident Management

Given a detected token anomaly, when suspicious activity is identified, then the system sends a real-time notification to the security team and logs the incident for further review.

User-friendly Token Delivery Interface

"As an operator, I want a clear and straightforward interface for token delivery so that I can complete the secure login process quickly and efficiently."

Description

Develop an intuitive user interface that clearly displays the dynamically generated tokens during the login process, integrating seamlessly with the ReservoirSnap workflow to ensure users receive guidance and feedback for enhanced security and usability.

Acceptance Criteria

Real-time Token Display

Given the user navigates to the login screen, When the token is generated, Then the token is displayed clearly with proper formatting and legibility.

Intuitive Interface Feedback

Given that a token is generated during login, When the token becomes invalid or expired, Then the interface displays clear, step-by-step guidance for corrective actions.

Seamless Integration with ReservoirSnap Workflow

Given a successful login and token validation, When the token details are presented, Then the interface integrates dynamically with the ReservoirSnap dashboard ensuring real-time operational feedback.

Adaptive Access

Implement adaptive authentication protocols that adjust security measures based on user behavior and contextual data. This feature balances robust security with user convenience, tailoring the authentication process to the risk level associated with each access attempt.

Requirements

Contextual Risk Analysis

"As an oil and gas field operator, I want the system to assess the context of my login attempts so that I can receive the appropriate security measures without hindering my workflow."

Description

This requirement implements a mechanism to evaluate contextual user data such as location, device, and activity patterns to determine the risk level of each access attempt. The module integrates with ReservoirSnap, enhancing adaptive authentication by applying tiered security measures based on real-time risk analysis. It reduces potential vulnerabilities while preserving an optimal user experience.

Acceptance Criteria

Location Risk Analysis

Given a user attempts access from a high-risk geographic area, when the system evaluates the location data, then the risk level is elevated prompting additional verification steps.

Device Risk Analysis

Given a user accesses the system from an unrecognized or potentially compromised device, when the system examines the device details, then it assigns a higher risk level and triggers added security protocols.

Activity Pattern Anomaly Detection

Given a user's behavior deviates from their typical activity pattern, when the system assesses the contextual risk by comparing historical access data, then it prompts multi-factor authentication to validate the user.

Real-Time Risk Score Computation

Given an access attempt is made, when the system consolidates real-time contextual data (location, device, activity), then a risk score is computed and passed to the adaptive authentication module for decision-making.

Integration with Adaptive Authentication

Given that the Contextual Risk Analysis module determines a risk level, when the result is communicated to the adaptive authentication system, then tiered security measures are applied based on the computed risk.

Dynamic Authentication Workflow

"As an engineer, I want the login process to adapt to my risk profile so that I can access the system securely without facing unnecessary obstacles."

Description

This requirement establishes an authentication workflow that dynamically adjusts based on the assessed risk level. It modifies traditional login procedures by adding or reducing authentication steps, such as two-factor authentication, security questions, or biometric checks, according to real-time user behavior and environmental context. This integration enhances security while optimizing user convenience in ReservoirSnap.

Acceptance Criteria

Risk-Based Authentication Initialization

Given a user initiates a login attempt, when the system evaluates real-time risk data from behavior and context, then the authentication workflow dynamically selects the appropriate authentication steps.

Dynamic Two-Factor Authentication Trigger

Given a login attempt with elevated risk indicators, when the assessed risk exceeds a pre-set threshold, then the system automatically enforces additional two-factor authentication.

Adaptive Authentication Steps Adjustment

Given a returning user with consistent low-risk behavior, when the risk evaluation confirms low threat levels, then the system reduces the number of authentication steps to streamline access.

Continuous Risk Assessment Update

Given an active user session, when there is a change in user behavior, location, or context, then the system re-assesses risk in real-time and adjusts authentication requirements accordingly.

User Behavior Analytics Dashboard

"As a security administrator, I want to access a dashboard of authentication metrics so that I can monitor user behavior and adjust security measures effectively."

Description

This requirement involves creating an analytics dashboard that visualizes user authentication patterns, risk assessment metrics, and adaptive flows. The dashboard will provide insights into authentication attempts, successful adaptive interventions, and potential security threats, enabling administrators and users to monitor and adjust security measures based on real-time data. Integration with ReservoirSnap’s backend ensures comprehensive monitoring and informed decision-making.

Acceptance Criteria

Real-Time Monitoring

Given an authenticated admin, when the dashboard loads, then all user authentication patterns, risk assessment metrics, and adaptive flows are displayed within 3 seconds.

Risk Assessment Validation

Given a simulated high-risk access attempt, when processed by the system, then the risk level is correctly calculated and displayed with an accuracy margin of ±5%.

Backend Integration Verification

Given a real-time update from the ReservoirSnap backend, when the dashboard refreshes, then the latest authentication data and adaptive security events are accurately reflected immediately.

Adaptive Intervention Tracking

Given an adaptive security event, when it is processed, then the dashboard logs successful adaptive interventions and flags unauthorized attempts, providing a clear overview to administrators.

Secure Session Manager

Monitor and manage active sessions in real time with automated session tracking and timely expirations. The Secure Session Manager provides an added layer of security by facilitating immediate session termination should any suspicious activity be detected.

Requirements

Real-Time Session Monitoring

"As a field operator, I want to view all active sessions in real time so that I can promptly detect and respond to any security anomalies."

Description

The feature provides a real-time dashboard to track and display all active sessions within ReservoirSnap, including session start time, duration, and user activity. This integration with the product ensures field operators and engineers can quickly identify anomalies and manage session behaviors effectively.

Acceptance Criteria

Active Session Dashboard Display

Given a ReservoirSnap operator is logged in, when they access the real-time dashboard, then the system shall display a list of all active sessions with accurate session start times, durations, and user activity details.

Real-Time Session Anomaly Identification

Given multiple concurrent sessions, when a session exhibits unusual behavior or exceeds normal duration thresholds, then the system shall highlight and flag the session for operator review.

Automated Session Expiration

Given a session that is inactive or flagged as suspicious, when the expiration criteria are met, then the system shall automatically terminate the session and update the dashboard in real-time.

User-Initiated Session Termination

Given that an operator or administrator identifies a compromised session, when they initiate session termination from the management console, then the system shall immediately end the session and record the event for audit purposes.

Automated Session Expiration

"As an engineer, I want sessions to automatically expire after a period of inactivity so that I can ensure system security without the need for manual intervention."

Description

The system will automatically terminate sessions after a pre-defined period of inactivity, reducing the risk of unauthorized access. This requirement is essential to maintain security standards and ensure that inactive sessions do not become vectors for potential breaches within the ReservoirSnap environment.

Acceptance Criteria

Idle Session Timeout

Given a user session is inactive for the predefined period, when the inactivity threshold is met, then the system automatically terminates the session.

Suspicious Activity Detection

Given an active session, when suspicious activity is detected, then the system immediately terminates the session to mitigate potential security risks.

Configurable Timeout Parameter

Given an administrator sets a custom inactivity timeout value, when the new timeout period elapses without activity, then the system automatically expires the session according to the configured value.

Graceful Session Termination and Logging

Given a session expiration event, when the session is terminated due to inactivity, then the system logs the event with a timestamp and termination reason for audit purposes.

Real-Time Session Monitoring

Given multiple active sessions in the ReservoirSnap environment, when sessions reach the inactivity threshold concurrently, then the system should reliably terminate all such sessions without performance degradation.

Suspicious Activity Alerts

"As a field operator, I want to receive immediate alerts for any suspicious session activities so that I can take timely action to secure the system."

Description

Integrate AI-driven anomaly detection to monitor session behaviors in real time. Upon detecting suspicious patterns, the system will trigger automatic alerts, enabling immediate investigation and response. This enhances security by facilitating early detection of potentially compromised sessions.

Acceptance Criteria

Real-Time Suspicious Alert

Given a session exhibiting unusual behavior, when the AI anomaly detection module processes the session data, then an immediate alert is triggered and logged.

Automated Session Termination

Given a session with confirmed suspicious activity, when the system validates the threat, then the session is automatically terminated and the user is logged out.

Detailed Activity Logging

Given the detection of suspicious behavior, when the alert is generated, then a comprehensive log entry is created capturing session details, timestamp, and event context.

User Notification Escalation

Given a newly detected suspicious session, when the alert is triggered, then a notification is sent to system administrators for immediate review and response.

Anomaly Detection Accuracy

Given a test session with pre-defined anomalous behavior, when the system processes the behavior, then the AI anomaly detection must correctly identify it as suspicious with a 95% detection rate.

Immediate Session Termination

"As an administrator, I want the ability to terminate any session immediately when suspicious activity is detected so that I can secure the system promptly."

Description

Allow both automated and administrator-initiated termination of sessions identified as security risks. This functionality provides a direct mechanism to instantly disconnect potentially compromised sessions, thus mitigating immediate threats and integrating seamlessly with ReservoirSnap’s broader security protocols.

Acceptance Criteria

Automated Threat Detection Trigger

Given a user session exhibiting suspicious behavior, when ReservoirSnap's AI threat detection flags the session as a risk, then the system automatically terminates the session and logs the event with a timestamp and reason.

Administrator Initiated Session Termination

Given that an administrator reviews session alerts, when the admin selects the terminate option in the Secure Session Manager, then the session is immediately disconnected with a confirmation prompt displayed and recorded in the audit log.

Immediate Notification After Termination

Given a session is terminated either automatically or manually, when the termination occurs, then the system sends real-time notifications to the designated security team and logs the termination details in the system dashboard.

Audit Logging for Terminated Sessions

Given a session has been terminated for security reasons, when an audit is performed, then the system must show detailed log entries including the time, method of termination, and reason for termination to ensure compliance.

Integration with ReservoirSnap Security Protocols

Given a session termination event, when it is triggered, then the termination must automatically integrate with ReservoirSnap’s broader security protocols by synchronizing data with relevant modules and updating security status across the platform.

Session Activity Audit Logging

"As a compliance manager, I want to have access to comprehensive audit logs of session activities so that I can review and analyze events for security and regulatory compliance."

Description

Record detailed logs of all session events, including logins, terminations, and anomalies. These logs are essential for compliance audits and forensic investigations and will integrate with ReservoirSnap’s analytics systems to provide historical tracking of session behaviors.

Acceptance Criteria

Session Login Event Logging

Given a valid session, When a user logs in, Then the system must record a log entry with the timestamp, user identifier, and session ID.

Session Termination Logging

Given an active session, When the user or system terminates the session, Then the system must record the termination event with the termination timestamp and reason.

Suspicious Activity Anomaly Logging

Given unusual session behavior, When anomalies such as multiple failed logins or IP shifts are detected, Then the system must log the event with detailed contextual information and trigger an alert if thresholds are exceeded.

Analytics System Integration Logging

Given a session event, When the log entry is created, Then it must be formatted according to the ReservoirSnap analytics specifications and forwarded to the analytics system within 30 seconds.

Compliance Audit Log Records

Given a compliance audit request, When the audit logs are retrieved, Then the system must provide detailed records including timestamps, event types, and user identifiers, ensuring data is securely exportable.

Audit Log Tracker

Keep comprehensive, immutable logs of all authentication and access events. The Audit Log Tracker ensures compliance and streamlined troubleshooting by allowing administrators to review detailed records of who accessed ReservoirSnap, when, and under what conditions.

Requirements

Comprehensive Event Logging

"As an administrator, I want all access events logged with complete detail so that I can review historical access data and perform audits to ensure system integrity."

Description

The system must capture every authentication and access event with details including user identity, timestamp, event type, and source. This functionality ensures that all interactions are recorded in an immutable log, supporting compliance, troubleshooting, and forensic analysis. The event logging system should integrate seamlessly with the ReservoirSnap operational backend and provide high availability under peak load.

Acceptance Criteria

User Authentication Logging

Given a valid login attempt, when a user successfully logs in, then record the user identity, timestamp, event type as 'login', and the source, ensuring the entry is immutable.

User Failed Authentication Logging

Given an invalid login attempt, when a user's authentication fails, then capture any provided user identity, timestamp, event type as 'failed login', source, and error details for forensic purposes.

Sensitive Operation Access Logging

Given an attempt to access a sensitive operation, when the request is made, then log the user identity, timestamp, event type as 'access attempt', and operation details to ensure compliance and auditability.

High-Load Logging Performance

Given peak load conditions where multiple events are generated concurrently, when the system processes these events, then ensure all events are captured, logged and remain immutable without any performance degradation.

Audit Log Integration Validation

Given events captured by ReservoirSnap, when these events are stored and integrated with the operational backend, then verify high availability and consistent log integrity across systems.

Immutable Log Storage

"As a compliance officer, I want the log data to be stored immutably so that it remains trustworthy and meets audit requirements."

Description

The system must store logs in a secure, tamper-proof repository that prevents unauthorized modifications. This feature provides data integrity and supports compliance with regulatory standards. The log storage solution should be designed to scale with increasing data volumes, and incorporate encryption at rest and in transit.

Acceptance Criteria

Secure Log Storage

Given a log entry is created, when the log is written, then the system must store the log in an immutable repository that prevents any unauthorized modifications.

Encryption Enforcement

Given a log entry is recorded, when the log is stored, then encryption must be applied at rest and in transit, adhering to industry-standard encryption protocols.

Scalability Under Load

Given high volumes of log entries, when the system experiences peak load, then it must horizontally scale without any data loss or performance degradation.

Compliance Audit Readiness

Given an audit is initiated by an administrator, when accessing logs, then the system must provide a comprehensive, tamper-proof audit trail with traceable metadata.

Unauthorized Access Prevention

Given a log access request, when unauthorized access is attempted, then the system must deny access and record the attempt for security review.

Log Query Interface

"As an administrator, I want to filter and search log entries easily so that I can quickly pinpoint and investigate specific system events."

Description

The system should include a user-friendly interface that allows administrators to search, filter, and analyze log data efficiently. The query capabilities must support multi-parameter searches, date range filters, and advanced sorting options, facilitating quick insights and troubleshooting. The feature should integrate with the audit log storage for near real-time data retrieval.

Acceptance Criteria

Basic Search Functionality

Given an administrator with valid credentials, when they enter a search term in the Log Query Interface, then the system returns a list of matching logs sorted by relevance within 2 seconds.

Filter by Date Range

Given an administrator, when they apply a start and end date filter in the Log Query Interface, then the system displays logs within the specified date range with accuracy to the second, updating results in under 1 second.

Multi-Parameter Search and Advanced Sorting

Given an administrator, when they use multiple parameters (such as user ID, event type, and date range) and apply advanced sorting options, then the system accurately retrieves and sorts the log data based on the specified parameters without errors.

Real-Time Log Alerts

"As an administrator, I want to receive notifications on abnormal access activities so that I can react quickly to potential security breaches."

Description

The system should offer real-time alerts triggered by specific patterns or anomalies in the log data. These alerts should notify administrators immediately via email or SMS when suspicious activities or critical events occur, thereby enabling proactive intervention. The alerting feature should be highly configurable to adapt to different operational requirements and compliance mandates.

Acceptance Criteria

Suspicious Activity Alert

Given a log event matching defined suspicious patterns, When the event is recorded, Then an immediate alert is sent via email and SMS.

Critical Event Notification

Given a critical event recorded in the log (e.g. unauthorized access), When the event is detected, Then a high-priority notification is triggered via email and SMS.

Configurable Alert Settings

Given an administrator accesses the alert configuration page, When changes to alert thresholds are made, Then the new settings are saved and applied in real-time.

Accuracy and Performance

Given the real-time log feed, When multiple alerts exceed the configured thresholds, Then the system should accurately identify and notify about each event without performance degradation.

Audit Log of Alerts

Given that an alert is triggered, When the alert is issued, Then an immutable audit record is created in the Audit Log Tracker with complete alert details.

Log Export and Reporting Tool

"As an auditor, I want to export log data and generate detailed reports so that I can review compliance and system performance over time."

Description

The system must provide functionality to export log data in various formats (e.g., CSV, JSON) and generate customizable reports. This enables comprehensive auditing and historical analysis, supporting both internal reviews and external regulatory audits. The export tool should be integrated with the query interface to allow filtered exports and scheduled report generation.

Acceptance Criteria

Real-Time Export Execution

Given an authenticated user with export permissions, when the user selects the export option from the Audit Log Tracker, then the system must generate and download a CSV file containing filtered log data based on applied query parameters.

Customizable Report Generation

Given an administrator, when configuring a new report via the query interface with custom date ranges and specific filter options, then the system must generate a previewable JSON report that accurately reflects the selected criteria.

Scheduled Report Automation

Given an administrative user setting up a scheduled report, when the user inputs the schedule, format, and filter criteria, then the system must automatically export the log data in the selected format at the scheduled intervals and email the report to designated recipients.

Integration with Query Interface

Given an authenticated admin using the log query interface, when the user applies specific filters and requests data export in multiple formats, then the system must output the correctly filtered log entries in both CSV and JSON formats.

Audit Compliance and Traceability

Given an internal audit scenario, when an auditor reviews a log export activity, then the system must provide an immutable record detailing the export event including the trigger, applied filters, timestamp, and execution status for compliance verification.

Interactive Walkthrough

Guide new users instantly through ReservoirSnap’s key functionalities with a step-by-step, visual tour. This feature helps users familiarize themselves with the interface, reducing initial anxiety and empowering them to confidently navigate the software for real-time insights.

Requirements

Interactive Walkthrough Introduction

"As a new ReservoirSnap user, I want an engaging introductory walkthrough so that I can understand the system’s key features quickly and begin utilizing its capabilities without feeling overwhelmed."

Description

Provide a dynamic, step-by-step introduction to ReservoirSnap’s dashboard elements and controls, enabling first-time users to quickly grasp real-time insights, AI analytics, and operational adjustments. This integrated walkthrough is designed to reduce initial learning curves and accelerate confidence in using advanced monitoring tools effectively.

Acceptance Criteria

User Completes Walkthrough

Given a first-time user logs into ReservoirSnap, When they choose to start the interactive walkthrough, Then the system should sequentially highlight and describe each dashboard element and control.

User Resumes Walkthrough

Given a user exits the walkthrough mid-process, When they return to the tool, Then the system should restore the walkthrough at the last completed step to allow smooth resumption.

Performance and Load Assessment

Given a user starts the walkthrough, When each step loads, Then the UI should render the necessary components within 2 seconds to ensure a seamless experience.

Contextual Tip Bubbles

"As a user, I want context-sensitive tips during the walking tour so that I can learn the significance of each feature without needing to refer to external documentation."

Description

Implement contextual tooltips that appear at key interface elements during the walkthrough, providing succinct, actionable tips and guidance. These in-line hints help users understand specific functions and troubleshoot common issues instantly, thereby improving user experience and reducing support queries.

Acceptance Criteria

Initial Walkthrough Interaction

Given a new user launches the interactive walkthrough, when they focus on a highlighted interface element, then a contextual tooltip should appear providing a brief, actionable tip.

Contextual Visibility and Accuracy

Given the user is navigating the walkthrough, when a key interface element is displayed, then the contextual tip bubble must present an accurate, context-specific guidance message relevant to that element.

Responsive Behavior and Dismissal

Given a user is reading a contextual tip bubble, when the user interacts outside the tip or completes the guided action, then the tip bubble should automatically dismiss to maintain interface clarity.

Onboarding Progress Indicator

"As a new user, I want a progress indicator during the walkthrough so that I can see how much of the onboarding process I've completed and what remains to be learned."

Description

Develop a visible progress tracker within the interactive walkthrough that clearly shows users their current position and the remaining steps. This feature enhances transparency in the onboarding process and allows users to monitor their progress, thereby fostering a sense of accomplishment as they advance.

Acceptance Criteria

Initial Walkthrough Launch

Given a new user logs into ReservoirSnap for the first time, when the interactive walkthrough is launched, then the onboarding progress indicator must be displayed at the top of the interface.

Sequential Progress Updates

Given the user completes a step in the walkthrough, when they move to the next step, then the progress indicator should update to reflect the completed step and highlight the upcoming step.

Progress Tracker Visibility

Given a user is engaged in the interactive walkthrough, when viewing different sections of the tour, then the progress indicator must remain clearly visible and fixed in place without any overlap.

Accurate Step Count

Given the onboarding process has a set number of steps, when the user navigates through these steps, then the progress indicator must accurately show the current step number and the total steps available.

Responsive Design Consideration

Given a user accesses ReservoirSnap through various devices, when the walkthrough loads, then the progress indicator should adapt to different screen sizes and maintain consistent functionality.

Skip & Replay Options

"As a ReservoirSnap user, I want the ability to skip or replay sections of the walkthrough so that I can customize my learning experience according to my familiarity with the system."

Description

Integrate navigation controls that allow users to skip certain parts of the walkthrough or replay previous sections. This versatility meets the needs of experienced users who may already be familiar with the system, while also accommodating those who require repeated guidance through critical process steps.

Acceptance Criteria

Skip Functionality

Given a user is in the interactive walkthrough, when they press the skip button, then the system should immediately take the user to the next logical section without errors.

Replay Functionality

Given a user navigated through a section of the walkthrough, when they select the replay option, then the system should restart that selected section of the walkthrough from the beginning, ensuring all interactive elements are reinitialized.

Navigation Controls Visibility

Given the walkthrough’s start, when the system presents the interactive tour, then the navigation controls including skip and replay options must be visually highlighted and easily accessible on screen.

User Preferences Persistence

Given that a user selects skip or replay during a walkthrough session, when the user resumes the walkthrough on a subsequent session, then the system should store and reflect these choices for user convenience.

Error Handling

Given a user interacts with the skip or replay options, when an unexpected error occurs during loading the new section or replaying the section, then the system should display an informative error message and provide a mechanism to retry the operation.

Gamified Training

Integrate gamification elements such as points, challenges, and rewards during onboarding. This boosts engagement and motivation, transforming the learning process into an enjoyable experience while accelerating mastery of ReservoirSnap’s advanced features.

Requirements

Gamification Onboarding Flow

"As a new ReservoirSnap user, I want an engaging onboarding flow that incorporates gamification so that I can quickly learn and effectively utilize the platform's features."

Description

Design an interactive gamified training module that guides new users through ReservoirSnap's advanced features. The module will utilize points, challenges, and immediate feedback to enhance engagement, ensuring users quickly understand and master critical functions. This feature is integrated into the onboarding process, aiming to reduce training time and improve user retention.

Acceptance Criteria

User Onboarding Flow Initiation

Given a new ReservoirSnap user, when the onboarding flow is initiated, then the system must display a clear introduction with an overview of gamification benefits, initial points status, and a preview of upcoming challenges.

Gamified Challenge Completion Feedback

Given that a user completes a gamified challenge, when the challenge concludes, then the system must immediately display feedback including points earned, any achievements unlocked, and guidance to the next challenge.

Progress Tracking and Rewards Display

Given a user is engaged in the onboarding module, when the user progresses through challenges, then the system must present an intuitive dashboard showing cumulative points, current progress, unlocked rewards, and upcoming milestones.

Seamless Integration with Onboarding Platform

Given the gamified training module is launched, when a user navigates through the onboarding process, then the module must integrate smoothly with ReservoirSnap's main navigation while preserving session data across training stages.

Responsive Design Across Devices

Given that the gamified training module is accessed from various devices, when a user logs in on mobile, tablet, or desktop, then all interactive elements and layouts must adjust responsively to provide a consistent and engaging experience.

Dynamic Challenge System

"As an experienced ReservoirSnap user, I want adaptive challenges that match my current skill level so that I can continuously improve my understanding of advanced features."

Description

Develop a dynamic challenges system that personalizes learning by offering real-time, performance-based challenges. This system will adjust the difficulty based on user performance, encourage problem-solving related to well monitoring, and provide instant feedback and progression hints. It will foster a competitive environment that motivates continuous improvement.

Acceptance Criteria

Real-time Challenge Adaptation

Given a user's performance metrics are tracked, when the user successfully completes a level, then the system should adjust subsequent challenge difficulty based on real-time performance data.

Feedback Delivery and Hints

Given a challenge is completed unsuccessfully, when the user fails to meet the expected performance threshold, then the system must provide actionable, instant feedback and progression hints to help the user improve.

Integration with Gamified Onboarding

Given users are undergoing gamified training, when the dynamic challenge system is triggered, then the system should seamlessly integrate game elements without disrupting the core onboarding process while accurately tracking performance metrics.

Reward and Achievement Mechanism

"As a ReservoirSnap user, I want a clear reward system with achievements so that my progress is recognized, encouraging me to fully engage with the training program."

Description

Implement a reward system that assigns badges, unlocks new levels, and rewards tangible incentives when users meet key training milestones. This system will incorporate leaderboards to foster competition and recognition, driving users to engage more deeply with the training content. The mechanism will align with ReservoirSnap’s goal to maximize user engagement and foster continuous professional development.

Acceptance Criteria

Badge Milestone

Given a user completes a training module milestone, when the milestone is achieved, then a badge must be automatically awarded and persistently displayed on the user's profile.

Level Unlocking

Given a user accumulates sufficient points, when the threshold is met, then the system should unlock the next training level within 5 seconds.

Tangible Incentive Trigger

Given a user finishes core training modules, when the user qualifies for tangible rewards, then the system must allocate rewards and trigger an email notification detailing the incentive.

Leaderboard Update

Given a user completes a challenge, when points are awarded, then the leaderboard must update in real-time to reflect the new ranking.

Real-Time Reward Notification

Given a user reaches a key training milestone, when the reward criteria are met, then a real-time notification must be displayed with details of the earned reward including points and badges.

Progress Tracking Dashboard

"As a ReservoirSnap user, I want to see a progress tracking dashboard so that I can monitor my learning journey and focus on areas that need improvement."

Description

Create a comprehensive dashboard that visually tracks user progress throughout the gamified training. The dashboard will display metrics such as earned points, completed challenges, and achieved milestones, providing users with insights into their learning journey. This feature will help users identify improvement areas and motivate further engagement with ReservoirSnap's functionalities.

Acceptance Criteria

Dashboard Overview

Given a gamified training environment, when a user logs into the dashboard after completing a training session, then the dashboard should display updated earned points, completed challenges, and achieved milestones.

Real-Time Data Refresh

Given that the user is actively engaged in the gamified training, when they complete each challenge, then the dashboard should automatically update with the new metrics in real time.

User Progress Visualization

Given that a user finishes multiple sessions, when they view the progress tracking dashboard, then the dashboard visual elements (charts, graphs, etc.) should clearly indicate trends in performance metrics over time.

Error Handling and Data Integrity

Given intermittent connectivity during gamified training, when data fails to update, then the system should display an appropriate error message and attempt a data resync without data loss.

Simulation Mode

Offer a safe, sandbox-like environment where users can practice real-world scenarios and troubleshooting without risk. By mimicking actual operations, Simulation Mode enhances learning outcomes and builds user confidence in handling ReservoirSnap's sophisticated tools.

Requirements

Simulation Environment Setup

"As an oil field operator, I want a realistic simulation environment so that I can practice and improve my troubleshooting skills without impacting real operations."

Description

Develop a comprehensive simulation sandbox environment in ReservoirSnap that mirrors actual operational conditions while ensuring user operations remain risk-free. This simulation environment allows users to adjust parameters such as sensor readings, flow rates, and pressure levels to practice troubleshooting and operational management. Integration with ReservoirSnap’s core monitoring tools ensures data consistency and real-time insights.

Acceptance Criteria

Parameter Adjustment and Simulation Accuracy

Given the user is in Simulation Mode, when they adjust parameters such as sensor readings, flow rates, and pressure levels, then the simulation updates accurately and reflects real-time changes consistent with ReservoirSnap's core monitoring tools.

Risk-free Operation Isolation

Given the user is operating in the simulation sandbox, when simulation parameters are modified, then the changes must not affect actual operational data or trigger real-time alerts in the production environment.

Real-Time Data Consistency

Given the integration with core monitoring tools, when simulation parameters are manipulated, then the updates should be immediately visible on real-time dashboards without compromising live data integrity.

User Training and Troubleshooting

Given the simulation is used for training, when a user initiates a troubleshooting scenario, then the system must provide realistic simulated feedback and response times that mirror actual operational conditions.

Data Logging and Replay Capability

Given a completed simulation session, when the session ends, then all parameter changes and simulation events must be logged and available for playback and post-session analysis to support training and review.

Scenario Library Integration

"As a training coordinator, I want to access a library of real-world scenarios within the simulation mode so that I can prepare our team for various emergency situations effectively."

Description

Integrate a diverse library of pre-configured simulation scenarios within ReservoirSnap that capture common operational, emergency, and abnormal conditions. Each scenario includes detailed metadata outlining its context, operational parameters, and expected outcomes, thereby streamlining user training and ensuring exposure to a broad spectrum of real-world situations.

Acceptance Criteria

Pre-Configured Operational Scenario Access

Given the user is logged into ReservoirSnap in Simulation Mode, when the user selects the Scenario Library, then all pre-configured simulation scenarios are displayed with accurate metadata.

Scenario Metadata Verification

Given a simulation scenario is selected, when the user views its details, then all metadata fields (context, operational parameters, expected outcomes) are correctly populated and match predefined values.

Emergency Scenario Simulation Execution

Given the Simulation Mode is active, when the user selects an emergency simulation scenario, then the scenario executes with parameters replicating real-world emergency conditions and expected system responses.

Scenario Library Search and Filter

Given the user needs to find a specific scenario quickly, when a search term or filter is applied, then the library returns a correctly filtered list of scenarios in order of relevance.

Real-Time Feedback and Analytics

"As an engineer, I want immediate performance feedback in simulation so that I can quickly learn from mistakes and optimize my decision-making process."

Description

Implement real-time feedback mechanisms within the simulation mode to provide immediate insights into performance metrics such as response times and troubleshooting accuracy. This integration with AI-driven analytics enables users to identify areas for improvement and refine their skills, thereby enhancing overall operational decision-making.

Acceptance Criteria

Immediate Feedback on Performance Metrics

Given a user is engaged in Simulation Mode, When the user completes a simulated operation, Then the system must display real-time feedback on performance metrics (response time, troubleshooting accuracy) within 2 seconds.

Consistent Analytics Display

Given the user is interacting with the simulation environment, When AI analytics run during the simulation, Then the system should generate and display performance analytics using dynamic visualizations that update in real-time.

Seamless Integration of Feedback

Given a user receives real-time feedback, When the user makes an operational adjustment within the simulation, Then the system must log the decision alongside context-sensitive explanations and identify improvement areas.

Error-Free Real-Time Data Processing

Given high-volume simulated data input, When the simulation mode handles multiple consecutive operations, Then the system must process and update performance metrics without lag or errors.

User Progress and Skill Assessment

"As a manager, I want to track my team's performance in simulation training so that I can tailor additional training and ensure their operational readiness."

Description

Develop a tracking and assessment system for monitoring user progress within the simulation mode. This system should log interactions, score performance, and generate detailed reports on user competencies to support personalized learning paths and targeted training recommendations.

Acceptance Criteria

Simulation Completion Tracking

Given a user in Simulation Mode, when they complete a simulation session, then the system must log all interactions, performance scores, session duration, and simulation outcomes accurately.

Real-Time Performance Scoring

Given a user performing simulation tasks, when actions are executed, then the system shall update the performance score in real-time within 1 second to reflect current progress.

Personalized Learning Report Generation

Given a user who has completed multiple simulation sessions, when a progress report is requested, then the system generates a detailed report featuring cumulative performance, trend analysis, and personalized training recommendations.

Secure Data Handling

"As an IT administrator, I want assurance that simulation mode data is securely handled so that simulation exercises do not compromise data integrity or security."

Description

Establish robust security measures within the simulation mode to ensure that all simulated operational data is managed securely. This includes data encryption, secure authentication, and compliance with industry standards, thereby safeguarding data integrity and user privacy throughout training exercises.

Acceptance Criteria

Data Encryption Test

Given simulation mode is active, when data is generated, then all operational data must be encrypted using AES-256 encryption protocol.

User Authentication Check

Given simulation mode is active, when a user logs in, then the system must enforce multi-factor authentication and secure session management.

Compliance Verification

Given simulation mode is active, when simulated data is stored or transmitted, then it must comply with relevant industry standards such as NIST or ISO/IEC 27001.

Data Integrity Validation

Given simulation mode is active, when users access simulated operational data, then data integrity must be validated using secure hash algorithms and checksum methods.

Customized Learning Paths

Tailor onboarding experiences to match specific user roles such as Field Engineer, Production Manager, and Maintenance Specialist. This personalized training ensures that users receive role-relevant content, accelerating proficiency and streamlining the transition to expert usage.

Requirements

Role-Based Onboarding Module

"As a Field Engineer, I want a training module customized to my specific role so that I can efficiently learn how to use ReservoirSnap’s features relevant to my daily tasks."

Description

Develop a module that provides tailored onboarding experiences based on user roles such as Field Engineer, Production Manager, and Maintenance Specialist. This module should integrate interactive tutorials, role-specific content, and contextual guidance to ensure that users quickly become proficient with ReservoirSnap’s functionalities, leading to accelerated learning and optimal utilization of the system.

Acceptance Criteria

Field Engineer Onboarding Experience

Given a new user with the Field Engineer role, when they log in for the first time, then the system should initiate a customized onboarding process with interactive tutorials and role-specific content.

Production Manager Onboarding Experience

Given a new user with the Production Manager role, when they access the onboarding module, then the system must display tailored content addressing production management challenges and ReservoirSnap functionalities.

Maintenance Specialist Onboarding Experience

Given a new user with the Maintenance Specialist role, when they begin the onboarding process, then the system should deliver interactive tutorials and contextual guidance specific to maintenance tasks.

Interactive Tutorial Progress Tracking

Given an active onboarding session, when a user completes each step of an interactive tutorial, then the system should record progress and update the progress dashboard in real time.

Contextual Guidance Delivery

Given a user engaged in the onboarding process, when they request additional help on a ReservoirSnap functionality, then the system should provide dynamically tailored guidance based on the user role and current task.

Dynamic Content Delivery

"As a Production Manager, I want training materials that adapt to my role and current operational needs so that I can focus on the most relevant information without unnecessary distractions."

Description

Implement a system that dynamically serves tailored learning content based on the user's role and the context in which they operate. This functionality should adjust the training materials in real-time, ensuring that users are presented with the most relevant information and interactive features, thereby reducing complexity and improving learning absorption.

Acceptance Criteria

Field Engineer Onboarding

Given a user logged in as a Field Engineer, when the training system loads dynamic content, then the system displays interactive, role-specific training modules tailored to field operations.

Production Manager Onboarding

Given a user logged in as a Production Manager, when accessing the training platform, then the system serves dynamic content focused on production analytics and operational insights with interactive features.

Maintenance Specialist Onboarding

Given a user logged in as a Maintenance Specialist, when the training module is activated, then the system displays maintenance protocols, troubleshooting guides, and checklists that are relevant to maintenance tasks.

Real-time Content Adjustment

Given an operational context change during the training session, when the system detects new conditions, then it dynamically adjusts the content to present the most relevant and updated training information.

User Role Verification

Given a user’s successful authentication and role determination, when the system retrieves training modules, then it confirms the user's role and delivers content that matches their specific operational context.

Progress Tracking and Feedback

"As a Maintenance Specialist, I want to track my onboarding progress and receive constructive feedback so that I can identify my strengths and weaknesses and improve my skills effectively."

Description

Create an integrated progress tracking and feedback system within the learning paths that monitors user performance, provides real-time insights, and offers actionable feedback. This system should leverage ReservoirSnap’s analytics capabilities to help users understand their learning journey, highlight areas of improvement, and adjust their training path accordingly.

Acceptance Criteria

Real-Time Performance Dashboard

Given a user enrolled in a learning path, when the user accesses the dashboard, then they should see real-time updates on their learning progress, including performance metrics, progress bars, and actionable insights derived from ReservoirSnap’s analytics.

Feedback Notification System

Given that a user completes a learning module, when their performance data is processed, then the system should automatically send real-time feedback notifications highlighting strengths and areas for improvement.

Adaptive Learning Path Adjustment

Given that a user's performance falls below set benchmarks, when the system evaluates their learning journey, then it should recommend adjustments to the learning path by suggesting supplemental training modules and targeted improvement resources.

User Progress Archiving and Retrieval

Given that a user has engaged in multiple training sessions, when the user requests historical performance data, then the system should present an organized archive of past performance metrics along with trends and summaries.

Multi-Device Accessibility

"As an oil and gas field operator, I want to access my training materials on any device so that I can engage with the content regardless of my location or preferred device."

Description

Ensure that the customized learning paths are optimized for multi-device access, including mobile phones, tablets, and desktop computers. This requirement involves creating a responsive design that delivers consistent user experience across different platforms, making training accessible anytime and anywhere.

Acceptance Criteria

Mobile Access During Field Operations

Given a user logs into the customized learning paths on a mobile device, when they view the course content, then the content should automatically adjust to the screen resolution ensuring full content visibility and responsiveness.

Tablet User Interface Consistency

Given a user accesses the customized learning paths via a tablet, when navigating through the modules, then the system should provide a consistent UI layout comparable to desktop and mobile devices with no functionality loss.

Desktop Reliable Experience

Given a user on a desktop computer, when accessing the training modules, then they should experience optimized, high-resolution interfaces with interactive media and no loading delays, ensuring consistency with mobile and tablet formats.

Responsive Performance under Varying Network Speeds

Given a user accessing the learning path on any device under low-bandwidth conditions, when the system loads content, then it should ensure a fast and responsive experience with adaptive content delivery that maintains integrity across devices.

Cross-Platform Seamless Transition

Given the user moves from one device to another, when transitioning between mobile, tablet, and desktop, then the customized learning path should save progress and maintain session continuity using responsive design and synchronized data.

In-app Knowledge Base

Provide a comprehensive, context-sensitive help center with tutorials, FAQs, and troubleshooting guides accessible directly within the app. This feature supports continuous learning and quick problem resolution, ensuring users can always find answers when they need them.

Requirements

Context Sensitive Search

"As an operator, I want to search help articles based on the current context of my work so that I can quickly find relevant guides to resolve issues."

Description

This requirement implements a context sensitive search within the in-app knowledge base that tailors search results to the specific context of the user's operation. It integrates advanced filtering and relevance ranking to display the most helpful tutorials, FAQs, and troubleshooting guides based on current system use, improving efficiency and reducing downtime.

Acceptance Criteria

Real-Time Context Search

Given a user operating within a specific module, when they input a keyword in the in-app knowledge base search bar, then the system returns a list of contextually relevant tutorials, FAQs, and troubleshooting guides prioritized by current operational data.

Filtering and Sorting Accuracy

Given a user applying advanced filtering options, when the filters are set for specific topics and content types, then the search results are accurately ranked and display only the most relevant content for the current context.

Performance Under Load

Given a scenario of high query load during peak operational hours, when multiple users conduct concurrent searches, then the system maintains a response time within 2 seconds while ensuring accurate contextual relevance of search results.

Guided Tutorial Integration

"As a field engineer, I want to access guided tutorials directly within the app so that I can efficiently perform complex tasks and resolve issues without external assistance."

Description

This requirement enhances the knowledge base with integrated guided tutorials that provide step-by-step walkthroughs for complex tasks and equipment operations. It automatically suggests relevant tutorials based on user activities and monitored system data, facilitating immediate learning and on-the-job support.

Acceptance Criteria

User Initiates Guided Tutorial

Given a first time user attempts a complex operational task, When they select the guided tutorial prompt, Then the system should display a step-by-step walkthrough contextualized to the task.

Context-Sensitive Tutorial Suggestion

Given the user's current activity and monitored system data, When the system identifies a need for assistance, Then it should automatically suggest the most relevant guided tutorial from the in-app knowledge base.

Completion and Progress Tracking

Given a user completes a guided tutorial, When the final step is reached, Then the system should mark the tutorial as completed, log the completion timestamp, and trigger an optional review prompt.

Tutorial Access Across Devices

Given a user logs into the ReservoirSnap app on multiple devices, When they access a guided tutorial, Then the tutorial progress is synchronized across all devices without disruption.

Error Handling for Guided Tutorials

Given a guided tutorial fails to load due to system errors, When an error is detected, Then a clear error message is displayed along with an option to access a static version of the help guide.

FAQ Dynamic Update

"As a well operator, I want the FAQ section to dynamically update based on recent common issues so that I can quickly access the most effective solutions."

Description

This requirement involves implementing a dynamic FAQ section that updates in real-time based on user interactions and feedback. It ensures the most frequently asked questions and their answers are prioritized and refined continuously, enhancing the relevance and reliability of support content for well operations.

Acceptance Criteria

Real-time Feedback Update

Given the user submits feedback through the FAQ section, when the feedback is processed, then the relevant FAQs are dynamically reordered and updated in real-time to reflect user input.

User Interaction Trigger

Given the user interacts with an FAQ entry, when the action is detected, then the system logs the interaction and triggers the dynamic update mechanism within 2 seconds.

Prioritization Algorithm Accuracy

Given a set of user interactions, when the prioritization algorithm runs, then the FAQs are rearranged based on interaction frequency, ensuring that the top three most accessed FAQs meet a minimum 30% higher interaction rate compared to others.

Offline Access to Help Content

"As an engineer in a remote location, I want to access the help content offline so that I can continue working effectively even without reliable network connectivity."

Description

This requirement enables offline accessibility for the in-app knowledge base, ensuring that users in remote or connectivity-challenged environments can still access essential tutorials, FAQs, and troubleshooting guides. The feature is critical for maintaining continuous operational support when network availability is limited.

Acceptance Criteria

Offline Access Functionality

Given the user has enabled offline mode on ReservoirSnap, when the device is disconnected from the network, then the in-app knowledge base content such as tutorials, FAQs, and troubleshooting guides should be fully accessible.

Content Caching and Storage

Given that the knowledge base content is updated and available online, when the device goes offline, then the latest available content should be cached and stored for offline access.

User Notification on Offline Mode

Given that the device loses connection, when the user attempts to access online-only content, then a clear notification should inform the user that they are in offline mode and that limited content may be available.

Content Synchronization Upon Reconnect

Given the user reconnects to the internet, when the connection is restored, then the knowledge base content should automatically synchronize with the latest available online content.

Performance and Usability in Offline Mode

Given offline mode is active, when the user navigates through the knowledge base, then all content retrieval operations should complete within 2 seconds to maintain a responsive experience.

Feedback and Content Rating System

"As a user, I want to provide feedback and rate help articles so that the content can be continually optimized to meet real-world needs."

Description

This requirement adds a mechanism for users to provide feedback and rate knowledge base articles, enabling continual improvement of content quality. It collects user insights and ratings to help refine tutorials, FAQs, and troubleshooting guides, ensuring that content remains current, accurate, and user-centric.

Acceptance Criteria

Real-time Rating Functionality

Given a user is viewing a knowledge base article, when they submit a rating, then the system should immediately update the article's average rating and store the rating in the database.

User Feedback Submission

Given a user is reading a help article, when they provide textual feedback along with an optional star rating, then the system shall save the feedback associated with that article for later review and continuous improvement.

Feedback Moderation and Analytics

Given that multiple users have submitted feedback, when the system aggregates feedback data, then it should generate analytics reports summarizing average ratings, feedback counts, and flag any feedback for moderation according to defined community standards.

Failure Forecaster

Leverages cutting-edge machine learning algorithms to predict sensor failures before they occur. This feature empowers users to schedule proactive maintenance, minimizing unexpected downtime and ensuring continuous field operations for enhanced reliability.

Requirements

Predictive Sensor Analysis

"As an oil and gas field operator, I want the system to accurately predict sensor failures so that I can proactively schedule maintenance and avoid unexpected disruptions in operations."

Description

Leverages advanced machine learning algorithms to analyze incoming sensor data, identify patterns, and predict potential sensor failures. This requirement ensures that the Failure Forecaster can accurately forecast failures, thereby allowing operators to preemptively address issues, minimize downtime, and optimize maintenance schedules.

Acceptance Criteria

Real-time Sensor Data Analysis

Given sensor data is continuously streamed, when the machine learning algorithm processes the incoming data, then potential sensor failures are flagged within 5 seconds.

Maintenance Scheduling Prompt

Given a sensor failure is predicted, when the forecast is generated, then a maintenance alert is automatically issued with recommended scheduling priorities.

Accurate Failure Prediction

Given historical sensor performance data combined with current sensor data, when the predictive analysis is executed, then the system achieves a minimum prediction accuracy of 90% across a test set.

Visual Reporting Dashboard

Given a predicted sensor issue, when an operator accesses the dashboard, then the system displays clear metrics including prediction confidence, trend analysis, and failure likelihood.

Integration with Operational Workflow

Given a high-risk sensor anomaly is detected, when the prediction is confirmed, then the system integrates with existing maintenance scheduling tools and logs the event for follow-up actions.

Real-Time Data Integration

"As an engineer, I want real-time integration of sensor data so that the prediction model always has the latest information for accurate forecasting."

Description

Ensures that the system seamlessly integrates with live sensor data streams to provide up-to-date inputs for failure prediction. This requirement is critical for enabling the machine learning component to operate on the most current data, ensuring real-time accuracy and responsiveness.

Acceptance Criteria

Live Sensor Integration for Immediate Alert

Given live sensor data is received, when the system processes the data, then it must update the failure prediction algorithm within 2 seconds.

Continuous Monitoring for Anomaly Detection

Given a continuous sensor data feed, when the system detects anomalous patterns, then it should trigger proactive maintenance alerts with at least 95% accuracy.

Sensor Data Sync During Network Interruptions

Given an interruption in network connectivity, when the connection is re-established, then the system must correctly synchronize the missed sensor data within 5 seconds.

Proactive Maintenance Scheduling

"As a maintenance manager, I want the system to automatically schedule maintenance based on predicted sensor failures so that I can maintain continuous field operations and reduce downtime."

Description

Automatically initiates and manages maintenance scheduling based on the predictions provided by the Failure Forecaster. This ensures that maintenance is performed proactively, effectively reducing the risk of unexpected equipment failures and operational downtime.

Acceptance Criteria

Automated Maintenance Trigger

Given a failure prediction is generated by the Failure Forecaster, When the prediction exceeds the maintenance threshold, Then the system shall automatically initiate the proactive maintenance scheduling process.

Maintenance Notification Dispatch

Given that a maintenance schedule has been created, When the scheduling is confirmed, Then the system shall dispatch notifications to the maintenance team with all relevant schedule details.

Dynamic Rescheduling Based on Updated Predictions

Given updated failure predictions are received, When the prediction metrics change significantly, Then the system shall automatically adjust the maintenance schedule accordingly and alert the relevant stakeholders.

Dashboard Display Integration

Given that proactive maintenance has been scheduled, When an operator accesses the ReservoirSnap dashboard, Then the dashboard shall display the scheduled maintenance details along with current sensor status.

User Override for Maintenance Scheduling

Given that a scheduled maintenance event is in place, When a user opts to override the scheduled maintenance, Then the system shall allow the override, record the action in system logs, and require confirmation to proceed.

Alert Notification System

"As a field operator, I want to receive immediate alerts about potential sensor failures so that I can quickly initiate repairs and mitigate any operational risks."

Description

Implements a robust alert mechanism that notifies field operators and maintenance teams about impending sensor failures in real time. This feature is designed to facilitate prompt corrective measures and rapid response to emerging issues to ensure continuity in operations.

Acceptance Criteria

Real-Time Alert Triggering

Given a sensor starts showing anomaly signals, when the Failure Forecaster predicts an impending sensor failure, then the system immediately sends an alert notification to the designated field operators and maintenance teams.

Alert Delivery Accuracy

Given the prediction of a possible sensor failure, when the alert notification is generated, then it must accurately display the sensor’s identity, failure probability, and recommended corrective actions.

Multi-Channel Notification

Given the detection of an impending sensor failure, when the alert mechanism is triggered, then notifications must be sent simultaneously via email, SMS, and in-app messaging, ensuring consistency across all channels.

Dashboard Visualization Interface

"As a systems analyst, I want a clear visual dashboard that shows sensor performance and failure trends so that I can easily monitor system health and make data-driven decisions."

Description

Provides an interactive, user-friendly dashboard that visualizes sensor data trends, predicted failures, and scheduled maintenance. This interface enhances situational awareness by offering clear visual insights, thereby supporting informed decision-making and efficient resource allocation.

Acceptance Criteria

Real-time Sensor Data Visualization

Given the dashboard is accessed, when sensor data updates occur, then the visualization updates in real-time with latency under 2 seconds.

Predicted Failure Alerts Display

Given a sensor failure is predicted, when the forecast event is available, then the dashboard must display clear alert notifications with failure probability and recommended actions.

Scheduled Maintenance Tracking

Given scheduled maintenance entries are available, when the user views the dashboard, then maintenance events are clearly indicated and color-coded with due dates and statuses.

Interactive Data Trend Exploration

Given the interactive dashboard interface, when the user applies different time filters or data selections, then the visualization updates correspondingly to show the selected sensor trends and historical patterns.

Maintenance Optimizer

Automatically generates optimized maintenance schedules by analyzing real-time sensor data and historical performance trends. It improves operational efficiency by targeting timely interventions and reducing redundant maintenance, thus extending equipment life and boosting production.

Requirements

Real-time Sensor Data Integration

"As an oil and gas field operator, I want real-time sensor data integrated from all monitoring devices so that I can quickly identify anomalies and schedule maintenance proactively."

Description

Integrate real-time sensor data from diverse sources into a unified system that supports continuous monitoring, ensuring data consistency and immediate insights for maintenance scheduling. This integration is critical for enabling proactive interventions and reducing downtime by providing up-to-date performance metrics.

Acceptance Criteria

Real-time Data Flow Validation

Given that the system receives sensor data from multiple sources, when the data is ingested, then it must be validated for consistency and updated in the system within 5 seconds.

Sensor Source Integration Validation

Given integration with various sensor sources, when data is transmitted, then the system must normalize the data into a unified format and display real-time readings with 99.9% accuracy.

Maintenance Scheduling Trigger

Given continuous sensor monitoring, when pre-defined thresholds or anomalies are detected, then the system must automatically generate an optimized maintenance schedule and trigger alerts for immediate intervention.

Error Handling and Recovery

Given potential data transmission failures, when a sensor disconnection occurs, then the system must automatically attempt recovery, log the error, and ensure continuous monitoring without data loss.

Predictive Maintenance Analysis Engine

"As an engineer, I want a predictive analysis engine that analyzes both real-time and historical data so that I can anticipate failures and plan maintenance more efficiently."

Description

Develop an AI-driven analysis engine that processes historical performance trends alongside real-time sensor data to forecast equipment failures. This engine will leverage machine learning algorithms to generate actionable insights for planning maintenance interventions, ultimately reducing redundant maintenance and optimizing asset performance.

Acceptance Criteria

Real-Time Data Ingestion

Given the engine receives real-time sensor data, when the data is ingested, then the system must process and store the data within 5 seconds of arrival.

Historical Data Processing

Given historical performance trends, when the analysis engine processes the data, then it must correlate trends with past maintenance records with at least 95% prediction accuracy.

Failure Prediction Alert

Given the analyzed data, when the engine forecasts a potential equipment failure, then it must generate and send an alert within 1 minute of detection.

Maintenance Schedule Optimization

Given the actionable insights from the ML algorithms, when generating maintenance schedules, then the engine must reduce redundant maintenance events by at least 20% compared to the baseline.

System Performance and Efficiency

Given the integration of sensor and historical data, when the analysis engine is operational, then it must maintain a system uptime of 99% and handle concurrent data streams without performance degradation.

Automated Schedule Optimization

"As a maintenance manager, I want an automated scheduling tool that optimizes maintenance plans based on real-time and predictive data so that I can allocate resources efficiently and prevent equipment downtime."

Description

Implement a module that automatically generates and refines maintenance schedules based on dynamic inputs from predictive analytics and real-time condition monitoring. This optimization ensures maintenance is performed at the optimal time, thereby extending equipment life and maintaining production efficiency while reducing unnecessary operational interruptions.

Acceptance Criteria

Real-Time Data Analysis

Given sensor and historical performance data is available, when the module processes these inputs, then it must generate an optimized maintenance schedule that targets timely interventions.

Schedule Refinement Trigger

Given an existing maintenance schedule and significant deviation in real-time condition monitoring data, when updated inputs are detected, then the schedule is refined within 10 minutes to reflect new maintenance needs.

Optimal Maintenance Window Identification

Given predictive analytics are applied on historical and current sensor data, when the data analysis is completed, then the module identifies and recommends the optimal maintenance window to maximize equipment lifespan.

Automated Alerts and Schedule Updates

Given detection of abnormal sensor readings by the system, when this data change occurs, then the module automatically updates the maintenance schedule and notifies the operations team within 5 minutes.

Smart Sensor Analyzer

Continuously monitors sensor performance to detect subtle anomalies and early signs of degradation. This feature offers precise, actionable insights that help users address potential issues early, ensuring robust performance and improved safety standards.

Requirements

Real-time Data Processing

"As an engineer, I want to see live sensor data so that I can make quick adjustments to maintain system efficiency."

Description

Implement a system to continuously collect and process sensor data in real time, ensuring immediate performance insight and quick operational decision-making. This integration with the AI-driven analytics enables precise monitoring and seamless data flow throughout the system.

Acceptance Criteria

Real-Time Sensor Data Collection

Given that the sensor is active, when it generates data, then the system must collect and forward the data within 1 second to ensure real-time processing.

AI-Driven Anomaly Detection

Given a continuous sensor data stream, when an anomaly is detected by the AI engine, then the system shall alert the user within 2 seconds of detection.

Seamless Data Integration with Analytics

Given incoming sensor data, when processed by the system, then the data should be integrated into the analytics dashboard in real time, enabling immediate operational decision-making.

Fault Tolerance and Recovery

Given a temporary drop in sensor data transmission, when the connection is restored, then the system shall recover and retroactively process missed data to maintain historical accuracy.

Anomaly Detection Engine

"As a field operator, I want the system to automatically detect unusual sensor behavior so that I can address potential issues before they escalate."

Description

Develop an AI-powered engine that identifies subtle anomalies and early signs of sensor degradation by analyzing both historical and real-time data. This feature enhances reliability and safety by providing accurate, actionable insights that integrate with operational workflows.

Acceptance Criteria

Real-time Anomaly Detection

Given real-time sensor data, when the AI engine detects an anomaly, then an alert is generated within 5 seconds.

Historical Data Pattern Analysis

Given the availability of historical sensor data, when the analysis is conducted, then early sensor degradation patterns are identified with at least 95% accuracy.

Integration with Operational Workflows

Given the anomaly detection output, when the system integrates with the operational dashboard, then actionable insights are displayed in real-time with all necessary operational metadata.

Performance Alerting

Given continuous monitoring of sensor performance metrics, when a degradation in performance is detected, then the engine triggers a prioritized alert with detailed anomaly description and recommended corrective actions.

Predictive Maintenance Alerts

"As an operations manager, I want to receive proactive alerts about potential sensor failures so that I can schedule maintenance in advance and minimize disruptions."

Description

Create automated alert mechanisms that notify users of emerging sensor degradation trends or potential failures using predictive analytics. This requirement reduces downtime and improves safety by enabling proactive maintenance scheduling and quick response to potential issues.

Acceptance Criteria

Sensor Degradation Detection

Given sensor data is continuously monitored, when predictive analytics identify a degradation trend exceeding predefined thresholds, then an automated alert with sensor ID, timestamp, and predictive failure time is generated.

Proactive Maintenance Scheduling

Given an alert has been generated, when the operator views the maintenance dashboard, then the alert should include actionable maintenance scheduling recommendations prioritized by sensor risk level.

Real-time Alert Notification

Given a sensor anomaly is detected, when the system validates the alert condition, then an immediate notification must be sent via email and SMS containing detailed AI-driven insights.

Accurate Alert Threshold Adjustment

Given varying sensor performance baselines, when the system analyzes historical sensor data, then it should automatically adjust alert thresholds to maintain a false positive rate below 5% over a 30-day period.

Performance Dashboard

"As a field engineer, I want a clear and comprehensive dashboard that displays sensor analytics so that I can easily monitor system health and performance."

Description

Build an interactive dashboard that visualizes key sensor metrics, analysis results, and trend indicators. By aggregating real-time data and AI insights in an intuitive interface, this requirement enhances user engagement and facilitates efficient monitoring of sensor performance.

Acceptance Criteria

Initial Data Aggregation Display

Given the dashboard is launched and real-time sensor data is being streamed, when the system aggregates data, then it must display key sensor metrics, analysis results, and trend indicators accurately.

Interactive Data Filtering

Given a user interacts with the filtering controls, when filters are applied, then the dashboard should update dynamically to display only the relevant sensor metrics and trends.

Responsive Real-Time Updates

Given sensor data updates occur continuously, when new data is available, then the dashboard must refresh and display updated insights within 2 seconds.

Error and Alert Handling

Given a sensor anomaly or error occurs, when the system detects an issue, then the dashboard should alert the user with a clear, actionable notification and provide relevant error details.

Drill-down Detailed Analytics

Given a user selects a specific sensor metric, when they request more details, then the dashboard must provide a drill-down view with historical trends and comprehensive analytics.

Real-Time Alert Engine

Delivers immediate alerts via a dynamic notification system when potential sensor issues are detected. Field engineers and maintenance specialists benefit from rapid response capabilities, reducing downtime, and preventing cascading failures through swift action.

Requirements

Dynamic Alert Notification

"As a field engineer, I want to receive automated, real-time alerts so that I can quickly identify and address sensor issues before they lead to significant operational downtime."

Description

The system must provide a dynamic alert notification framework that instantly informs field engineers and maintenance specialists of potential sensor issues. This functionality will include automated alert triggers, real-time data processing, and integration with legacy systems. The framework should support multi-channel notifications, including email, SMS, and in-app alerts, and should be configurable to accommodate various sensor thresholds. This feature aims to minimize operational downtime by rapidly informing users of system anomalies, enabling preventive measures to mitigate cascading failures.

Acceptance Criteria

Automated Sensor Alert Trigger

Given sensor data exceeds configured thresholds, when a sensor anomaly is detected, then the system automatically triggers alert notifications across all selected channels.

Real-Time Data Processing

Given continuous sensor data input, when a potential issue is promptly identified, then the system processes and validates the alert in real-time with a latency of less than 2 seconds.

Multi-Channel Notification Delivery

Given the identification of a sensor anomaly, when an alert is generated, then notifications must be concurrently dispatched via email, SMS, and in-app notifications.

Legacy Systems Integration

Given the presence of legacy monitoring systems, when a sensor issue is detected, then the alert notification framework seamlessly integrates and communicates with legacy systems without data loss.

Custom Alert Settings Dashboard

"As a maintenance specialist, I want a user-friendly dashboard to customize sensor alert settings so that I can adjust notifications to align with the specific needs of my operations."

Description

The system should include an intuitive alert configuration interface that allows users to set custom thresholds and define the parameters for sensor alerts. This interface must facilitate the selection of specific sensors, adjust sensitivity levels, and choose the alert delivery method. Integrating this interface within the product's existing dashboard will give field engineers and maintenance teams the flexibility to tailor the alert system to their operational requirements, ensuring that notifications are both timely and relevant.

Acceptance Criteria

Custom Alert Settings Activation

Given a field engineer accesses the Custom Alert Settings Dashboard, when the engineer selects a specific sensor and adjusts its threshold and sensitivity settings, then the system should save the configuration, update sensor monitoring in real time, and display a confirmation message.

Custom Alert Threshold Configuration

Given the Custom Alert Settings Dashboard, when a user enters a custom threshold value for a sensor, then the system should validate the input, update the monitoring parameters, and trigger an alert if conditions are met.

Alert Delivery Method Selection

Given that multiple alert delivery methods are available, when a user selects an alert delivery method (e.g., SMS, email, push notification), then the system should update the alert configuration accordingly and send alerts using the chosen method.

Custom Sensor Selection and Settings

Given the dashboard interface, when a user chooses one or more sensors and applies unique custom settings, then the system should correctly reflect these selections, save the configurations, and apply the alert criteria to the specified sensors.

Interface Responsiveness and Compatibility

Given the alert configuration interface is integrated into the existing dashboard, when field engineers access the interface from different devices, then the system should ensure high responsiveness and consistent display of custom settings across desktops and mobile platforms.

Alert Acknowledgment System

"As an operations manager, I want to track and acknowledge alerts so that I can monitor response times and evaluate the effectiveness of our maintenance interventions."

Description

Implement an alert acknowledgment and tracking feature that logs every alert issued, user response times, and subsequent actions taken. This requirement will provide a detailed audit trail, facilitating performance reviews and operational analytics. By recording acknowledgment timestamps and resolution outcomes, the system enables continuous improvement in alert management processes and supports compliance with industry standards.

Acceptance Criteria

Alert Acknowledgment Logging

Given an issued alert, when a user acknowledges it, then the system logs the acknowledgment timestamp and user ID.

Response Time Tracking

Given an alert issued, when it is acknowledged, then the system calculates and records the time elapsed between alert issuance and acknowledgment.

Resolution Outcome Recording

Given an acknowledged alert, when a resolution action is completed, then the system logs the resolution outcome along with any user-entered notes.

Audit Trail Generation

Given multiple alerts over time, when an audit report is requested, then the system provides a detailed report including timestamps, user actions, response times, and resolution outcomes for compliance verification.

Data Integrity and Security

Given the alert log records, when data is queried, then the system ensures data integrity, secure access, and permission-based visibility to protect sensitive operational data.

Predictive Dashboard

Provides a clear, visual interface that aggregates forecasting data and key performance indicators. This intuitive dashboard enables production managers and data analysts to quickly interpret trends and make informed decisions that drive efficiency and production reliability.

Requirements

Real-time Data Aggregation

"As a production manager, I want to see the most recent sensor and performance data in real-time on the dashboard so that I can respond immediately to any anomalies."

Description

The system shall aggregate real-time sensor data, incoming logs from the monitoring wells, and integrate it with historical forecasting data to display on the Predictive Dashboard. This feature ensures that the information displayed is always current, enabling timely decisions to maximize production and prevent disruptions. It provides an automated, dynamic data flow essential for predictive analytics and operational efficiency.

Acceptance Criteria

Real-Time Sensor Data Flow

Given real-time sensor data is generated, when the data is received by the system, then it should be displayed on the Predictive Dashboard within 2 seconds to ensure current monitoring.

Automated Log Data Integration

Given the receipt of sensor logs and monitoring well logs, when the system aggregates these logs, then it should integrate them with historical forecasting data with at least 95% accuracy for the dashboard display.

Dynamic Historical Data Synchronization

Given the availability of historical forecasting data, when new real-time data is integrated, then the system must dynamically update the dashboard interface and ensure data consistency across all sources.

Interactive Data Visualization

"As a data analyst, I want interactive graphs and charts that allow me to manipulate and explore data points so that I can derive deeper insights into trending production metrics."

Description

The dashboard should offer interactive visualization tools that allow users to explore time series data, adjust data granularity, and drill down into specific metrics. This functionality enhances the user's ability to identify relevant trends and anomalies by providing intuitive controls such as filtering, zooming, and hovering for detailed information, simplifying the analysis of complex data sets.

Acceptance Criteria

Zoom and Filter Interaction

Given a view of time series data, when the user adjusts the zoom slider or applies a filter, then the visualization updates to display only the selected timeframe and matching data points, reflecting the adjusted granularity.

Data Drill-Down Interaction

Given a time series visualization, when a user clicks on a specific data point, then the system displays detailed metrics and historical trends associated with that data point to enable in-depth analysis.

Tool-tip and Hover Information

Given a time series chart, when the user hovers over any data point, then a tool-tip appears displaying additional details such as exact timestamp, metric values, and anomaly indicators with a response time under one second.

AI-Driven Insights Integration

"As an engineer, I want AI-generated insights and alerts on the dashboard so that I can anticipate potential issues and take proactive steps to prevent downtime."

Description

Integrate AI algorithms to process aggregated data and generate predictive insights and alerts. The dashboard will display risk levels, forecast potential disruptions, and offer recommendations for preventative measures. This integration enhances decision-making by enabling operators to proactively mitigate issues and optimize production scheduling.

Acceptance Criteria

Real-Time Alert Notification

Given aggregated sensor data and operational metrics, when the AI algorithm identifies risk levels exceeding predefined thresholds, then the dashboard must display a real-time alert notification with the risk level and accompanying preventative recommendations.

Predictive Trend Analysis

Given a combination of historical data and live operational inputs, when the AI processing completes the analysis, then the dashboard must update and display predictive trends and forecasts within 2 minutes with an accuracy benchmark of at least 90%.

Preventative Recommendation Display

Given that the AI algorithm detects potential disruptions or anomalies, when such a risk is identified, then the dashboard must automatically display tailored preventative recommendations alongside detailed risk analytics.

Interactive Dashboard Data Visualization

Given the availability of AI-driven insights on the dashboard, when a production manager or data analyst clicks on a specific alert or trend indicator, then the system must provide detailed drill-down visualizations in real time to facilitate further analysis.

Customizable Alert System

"As a production manager, I want to configure personalized alert settings so that I receive notifications when critical parameters fall outside optimal ranges."

Description

Implement a customizable alert system that allows users to set thresholds and notifications for critical parameters. The system will enable configurations for various alert types, including email, SMS, or in-app notifications, ensuring that users receive timely warnings about operational deviations and potential risks.

Acceptance Criteria

Threshold Configuration

Given a user is logged in and navigates to the alert configuration page, when the user sets a specific threshold for a parameter, then the system must save the threshold and display a confirmation message.

Notification Configuration

Given a user accesses the alert configuration panel, when the user selects one or more notification types (email, SMS, in-app), then the system must enable the selected notifications and provide a configuration preview.

Alert Triggering Test

Given a parameter exceeds the configured threshold, when the alert condition is triggered, then the system must send notifications via the configured channels and log the alert event.

Alert Modification and Deactivation

Given a user is modifying alert settings, when the user updates or disables an alert configuration, then the system must immediately reflect the changes on the dashboard and confirm the update with a notification.

RealTime Visualizer

An interactive, dynamic dashboard that visualizes real-time trends and key performance metrics. It empowers users to monitor operations granularly and make immediate, data-driven decisions, thereby optimizing production efficiency.

Requirements

Interactive Data Display

"As an oil field operator, I want a dynamic dashboard so that I can view and interact with real-time metrics for immediate operational decision-making."

Description

The RealTime Visualizer must provide a dynamic and interactive dashboard where users can view real-time operational metrics. It should support seamless drill-downs, highlighting key indicators and trends, and allow users to manipulate the visualization for enhanced data insight. This integration ensures that field operators have immediate access to essential data, enabling quick, informed decisions in a high-pressure environment.

Acceptance Criteria

Real-Time Data Monitoring

Given a field operator is logged into the RealTime Visualizer, when the dashboard loads, then the system must display all operational metrics in real-time with a refresh interval of under 5 seconds.

Interactive Drill-Down Functionality

Given a user is viewing the summary dashboard, when they select a key performance indicator, then the dashboard must enable a seamless drill-down to detailed metrics without page reloads.

Data Manipulation and Customization

Given a user accesses the dynamic dashboard, when they adjust filters or settings, then the dashboard must immediately update the visualizations to reflect the new criteria with no significant lag.

Real-Time Data Integration

"As an oil and gas field engineer, I want to see real-time sensor data integrated into the dashboard so that I can monitor operations without delays and respond to anomalies as they occur."

Description

The feature should incorporate a robust real-time data integration system that ingests and processes live sensor outputs and operational metrics. This integration must support low-latency and high-frequency updates, ensuring that the dashboard reflects the most current data available. The system should seamlessly connect with external data sources and guarantee data synchronization across the platform.

Acceptance Criteria

Live Sensor Data Feed Trigger

Given live sensor outputs are received continuously, when the system ingests data, then the RealTime Visualizer must update within 2 seconds to reflect the current sensor state.

External Data Source Connection

Given an external data source provides operational metrics, when a connection is established, then data must synchronize seamlessly and appear on the dashboard without additional user intervention.

High Frequency Data Processing

Given high-frequency data updates, when multiple data points are sent concurrently, then the system should process all incoming data with no loss and update the dashboard accurately.

Low-Latency Updates Verification

Given any operational metric change in real time, when the system processes the update, then the corresponding data on the dashboard must reflect the change within an acceptable latency threshold.

Customizable Alert System

"As an operator, I want to set alerts on my dashboard so that I receive immediate notifications when critical operational thresholds are surpassed."

Description

The Visualizer must include a customizable alert system that triggers notifications based on predefined thresholds and anomalies in the dataset. This feature will enable users to set personalized alerts that cater to specific operational metrics. The alert system should be integrated within the dashboard and configurable by users to ensure timely notifications for high-priority issues and significant operational deviations.

Acceptance Criteria

Alert Notification Setup

Given a user is on the alert configuration page, when they set a customized threshold for a specific operational metric, then the system should save the configuration and display a confirmation message.

Dynamic Threshold Alerts

Given a metric exceeds the predefined threshold, when an anomaly is detected by the system, then a notification must be triggered to alert the user through the configured channels within 2 seconds.

Dashboard Alert Integration

Given a user is monitoring real-time data on the dashboard, when an alert condition is met, then the alert should be prominently displayed on the dashboard with detailed information about the anomaly.

Configuration Persistence

Given a user has configured custom alert thresholds and notification settings, when the user logs out and logs back in, then all previously saved configurations should persist and be readily available.

User Feedback on Alert Status

Given a user receives a notification alert, when the alert is clicked, then the system should navigate the user to a detailed log view of the alert including actionable recommendations.

Metric Mapper

A feature that allows users to drill down into specific performance metrics with intuitive mapping tools. This clarity provides comprehensive insights into operational KPIs, simplifying complex data analysis for more informed decisions.

Requirements

Interactive Data Map

"As an oil field operator, I want to interactively visualize and map performance metrics so that I can quickly identify and address operational inefficiencies."

Description

Provides an interactive mapping interface for performance metrics, enabling users to visually drill down into specific KPIs. This interactive element integrates with the product's AI capabilities to offer quick identification of operational bottlenecks and real-time insights, thereby improving decision-making for field operations.

Acceptance Criteria

Map Loading Under High Data Volume

Given the interactive data map is triggered under a high data load, when the user accesses the map, then it should load within 3 seconds and display all critical KPIs accurately.

Real-Time AI Insights Integration

Given that the performance metrics are updated in real-time, when the interactive map refreshes, then the AI-driven insights must be overlayed and updated seamlessly without user intervention.

Interactive Drill-Down of Metrics

Given a KPI element is clicked on the interactive map, when the user drills down, then detailed sub-metrics related to the selected KPI should be displayed and be fully interactive for further analysis.

User-Friendly Navigation and Filtering

Given the interactive interface is active, when the user applies filters or sorts KPIs, then the display should update in real-time and maintain consistent usability and accessibility standards.

Dynamic Drill-Down

"As a field engineer, I want to drill down into mapped performance metrics so that I can uncover detailed trends and underlying operational issues."

Description

Enables users to drill down into detailed performance metrics by interacting with mapped data elements. This feature progressively reveals complex data layers, providing deeper insights and targeted analysis on demand, thereby enhancing diagnostic precision.

Acceptance Criteria

Interactive Data Element Drill

Given the ReservoirSnap Metric Mapper displays mapped data elements, When a user clicks on an element, Then the system must drill down to show detailed performance metrics related to that element in a progressive manner.

Progressive Data Layer Loading

Given a user requests further details from a drill-down, When the application initiates the data retrieval process, Then subsequent layers of metric details must be loaded incrementally, maintaining optimal system performance.

User Feedback During Drill-Down

Given a drill-down action is initiated, When the user interacts with the mapped visualization, Then the interface must show immediate visual feedback (e.g., a loading spinner or highlight) confirming that the drill-down process is in progress.

Real-Time Data Visualization Update

Given the user drills down to view detailed metrics, When the action is performed, Then the system must update the visualization in real-time with the latest performance data to ensure accurate diagnostics.

Error Handling and Recovery

Given the possibility of network interruptions or data retrieval errors during drill-down, When an error occurs, Then the system must display an appropriate error message and offer options to retry or cancel the operation without system failure.

Customizable KPI Filters

"As an operations manager, I want to customize KPI filters so that I can focus on the metrics most relevant to our production efficiency."

Description

Allows users to customize the display of KPIs by applying filters that align with their specific operational needs. This requirement streamlines the analysis process by focusing on the most impactful performance indicators, facilitating more targeted and effective decision-making.

Acceptance Criteria

Filter Application in Real-time Dashboard

Given the user accesses the Metric Mapper, when they apply custom filters to KPIs, then only the selected KPIs should be displayed on the dashboard with updated data.

Saving Customized Filter Settings

Given the user customizes KPI filters, when they choose to save their settings, then the custom filter configurations should persist across sessions.

Default Filter Reset Functionality

Given a user has applied custom KPI filters, when they click the reset button, then the KPI filters should revert to their default settings, displaying all available KPIs.

Error Handling on Invalid Filter Criteria

Given a user enters an invalid value for a KPI filter, when they apply the filter, then the system should display an error message indicating the issue without crashing or altering the current view.

Performance Response on Applying Filters

Given a user applies one or multiple KPI filters, when the system processes the filters, then the updated KPI data should be displayed within 2 seconds to facilitate timely decision-making.

Real-Time Data Syncing

"As an oil field operator, I want the system to sync data in real-time so that I can make informed decisions based on the latest performance metrics."

Description

Ensures that the mapping tool continuously updates with real-time data, integrating AI-driven insights to reflect current operational conditions. This capability minimizes downtime and supports timely decisions, mitigating risks with up-to-date information.

Acceptance Criteria

Live Operational Data Update

Given the Metric Mapper is active during live operations, when new real-time data is received, then the UI updates immediately with the latest sensor metrics within a lag of less than 2 seconds.

AI-Driven Insight Integration

Given that ReservoirSnap processes incoming data through its AI engine, when anomalies are detected, then the Metric Mapper displays updated AI-driven insights in alignment with current operational conditions without manual intervention.

Continuous Background Syncing

Given that the Metric Mapper operates continuously in the background, when periodic data refresh intervals occur, then the system seamlessly integrates real-time data updates into the mapping tool without affecting performance.

Exportable Insights Report

"As a field engineer, I want to export comprehensive performance reports so that I can share actionable insights with my team for further analysis."

Description

Facilitates the generation of detailed, exportable reports from the metric mapper, allowing users to share essential insights and analysis in both digital and printed formats. This feature supports post-analysis review and collaborative decision-making by ensuring actionable insights are easily distributable.

Acceptance Criteria

Digital Report Generation

Given the user selects the export function in Metric Mapper, when they choose the digital export option, then the system should generate a detailed PDF report including all relevant metrics and insights.

Print-Ready Report Export

Given the user opts for a print version, when they trigger the export action, then the system should produce a print-ready report with proper formatting, headers, and footers.

Filtered Data Export

Given the user applies specific filters within Metric Mapper, when they export the report, then the system should include only the filtered data sets in the generated report.

Data Integrity Verification

Given an exported report, when the user compares the report data with on-screen metric data, then all values should match accurately within acceptable tolerance.

Report Sharing via Email

Given the user completes report generation, when they select the share option and enter an email address, then the system should attach the report to an email and successfully deliver it to the provided recipient.

Trend Navigator

This tool tracks historical trends and leverages predictive analytics to forecast future performance patterns. By identifying both short- and long-term shifts, it enables proactive strategies and more precise operational adjustments.

Requirements

Historical Data Aggregator

"As an engineer, I want to access reliable historical data so that I can accurately analyze past performance trends and make informed decisions about operational improvements."

Description

This requirement involves aggregating historical well monitoring data from various sources. It focuses on extracting, cleaning, and storing historical data to support trend analysis. By ensuring high-quality historical datasets, it forms the foundation for accurate trend forecasting and is fully integrated with ReservoirSnap’s existing data management system to enhance decision-making.

Acceptance Criteria

Data Extraction Validation

Given historical data sources are connected, when the aggregation process runs, then all required fields and records must be extracted with at least 99% accuracy.

Data Cleaning Accuracy

Given raw historical data is imported, when cleaning algorithms are applied, then the dataset should be free from duplicates and anomalies, achieving a data quality of at least 95%.

Data Storage and Retrieval

Given cleaned historical data is ready, when stored in ReservoirSnap's data management system, then it must be retrievable within 2 seconds and fully integrated with trend analysis modules.

Predictive Analytics Engine

"As a field operator, I want to receive predictive insights about future performance trends so that I can adjust my operations proactively to mitigate risks and optimize production."

Description

This requirement covers the development and integration of an AI-driven predictive analytics module that leverages historical and real-time data. It utilizes advanced machine learning algorithms to forecast future performance by identifying underlying patterns. The goal is to provide timely and actionable predictive insights that facilitate proactive operational adjustments, thereby increasing overall efficiency.

Acceptance Criteria

Historical Data Analysis

Given the reservoir monitoring system has access to historical data, when the Predictive Analytics Engine aggregates and processes the data, then historical patterns are identified and flagged for review with a minimum accuracy of 90%.

Real-Time Data Integration

Given the continuous stream of real-time sensor data, when the Predictive Analytics Engine ingests the data, then it seamlessly integrates with historical data and updates forecasts within a 5-second latency window.

Predictive Insights Delivery

Given the processed data and identified trends, when the engine generates forecast outputs, then actionable predictive insights are delivered to the operator dashboard with a clear alert mechanism and visual indicators.

Trends Visualization Dashboard

"As an operator, I want a visual dashboard that clearly displays current trends and future forecasts so that I can quickly understand performance patterns and take appropriate actions."

Description

This requirement specifies the design and development of an interactive dashboard that visually displays historical trends and predictive forecasts. It includes user-friendly charts, graphs, and real-time data indicators, ensuring that complex data is easily interpretable. The dashboard is designed to integrate seamlessly with ReservoirSnap’s user interface, providing users with clear, actionable insights at a glance.

Acceptance Criteria

User Accesses Trends Dashboard

Given a logged-in ReservoirSnap user, when they navigate to the Trends Visualization Dashboard, then the dashboard displays user-friendly charts, graphs, and real-time data indicators that clearly present historical trends and predictive forecasts.

Dashboard Data Integration Test

Given the integration with ReservoirSnap’s data sources, when the dashboard is accessed, then all visual elements must update in real-time without delays or data inconsistencies.

Interactive Chart Navigation

Given that the dashboard includes interactive charts, when a user clicks or hovers over any data point, then a tooltip or detailed view must appear with precise data insights related to that point.

Predictive Forecast Accuracy

Given the predictive analytics engine, when the dashboard displays forecast data, then the predictions should match actual trends within a 5% error margin over a defined testing period.

Responsive Dashboard Performance

Given users accessing the dashboard from various devices, when the dashboard loads and is interacted with, then it should maintain a responsiveness of at least 95% and minimal latency across common browsers and mobile platforms.

Trend-Based Alert System

"As an engineer, I want to receive immediate alerts about significant trend changes so that I can quickly assess and respond to evolving field conditions."

Description

This requirement entails creating a dynamic alert system that monitors the trend data for significant deviations and emerging patterns. It should trigger real-time notifications when critical thresholds are reached or when anomalies are detected. The alert system will be fully customizable and integrated with ReservoirSnap, enabling immediate, actionable responses from field operators and engineers to prevent potential issues.

Acceptance Criteria

Real-Time Alert Activation

Given trend data monitoring is active and predefined thresholds are set, when the system detects a significant deviation in trend data, then an immediate, AI-driven alert is issued on ReservoirSnap.

Alert Configuration Customization

Given that users have access to customization settings, when an operator sets custom thresholds and notification preferences, then these settings are saved and successfully applied to the alert system.

Historical Trend Correlation

Given a repository of historical trend data, when the system identifies patterns that diverge from historical norms, then a predictive alert is generated indicating potential future anomalies.

Operational Notification Delivery

Given that an alert is triggered, when the alert system processes the notification, then the alert is delivered via multiple integrated channels (dashboard, email, SMS) with a precise timestamp and actionable instructions.

Alert Override and Acknowledgment

Given a triggered alert, when an operator acknowledges or overrides the alert through the interface, then the system updates the alert status accordingly and logs the action in the audit records.

Insight Alerts

A real-time notification system that sets dynamic thresholds for key performance values. It instantly alerts users to significant changes and anomalies, ensuring rapid responses to issues and preventing potential problems.

Requirements

Dynamic Threshold Settings

"As an oil and gas field operator, I want to configure dynamic thresholds for performance metrics so that I receive accurate alerts that reflect current operational conditions and prevent false alarms."

Description

The Dynamic Threshold Settings requirement enables users to define and adjust dynamic thresholds that automatically adapt to changing key performance values. This feature integrates historical baseline data with real-time readings, ensuring alerts are both timely and accurate, and empowers rapid operational responses to evolving conditions.

Acceptance Criteria

Real-Time Threshold Update Scenario

Given the integration of historical baseline and real-time readings, when new data is ingested, then the dynamic thresholds adjust within 2 seconds based on the latest input.

User-Defined Threshold Settings Scenario

Given a user inputs new threshold parameters, when the user confirms the changes, then the system applies and saves the updated dynamic thresholds for immediate use.

Anomaly Alert Trigger Scenario

Given key performance indicators deviate from normal ranges, when the dynamic threshold is surpassed, then an alert is automatically generated and delivered to the operator.

Historical Baseline Analysis Scenario

Given access to historical performance data, when the dynamic thresholds are computed, then the algorithm utilizes at least 30 days of data to determine baseline values.

Threshold Performance Reliability Scenario

Given varying real-time performance data, when the dynamic threshold adapts over time, then the system maintains a success rate of at least 95% in predicting critical alert conditions.

Real-time Alert Notifications

"As a well monitoring engineer, I want to receive instantaneous notifications on both mobile and desktop devices so that I can quickly address any anomalies detected in real-time."

Description

The Real-time Alert Notifications requirement details the implementation of instantaneous notification capabilities using push alerts and integrated messaging systems. This feature ensures that users are immediately informed of any deviations from set performance thresholds, enhancing responsiveness and operational efficiency.

Acceptance Criteria

Alert on Exceeding Threshold

Given a key performance indicator exceeds its dynamic threshold, when real-time monitoring detects the deviation, then a push alert and integrated message must be dispatched immediately.

User Acknowledgement of Alert

Given a user receives a real-time alert, when the user taps or clicks the notification, then the system should open a detailed report page and log the user’s acknowledgement.

Multiple Notification Channels

Given an anomaly is detected in key performance metrics, when an alert is triggered, then notifications should be delivered simultaneously via push notifications and in-app messaging.

Alert Configuration Update

Given a user updates the dynamic thresholds in the alert settings, when the configuration is saved, then the new thresholds must be applied immediately for all subsequent monitoring.

Intelligent Anomaly Detection

"As an operational manager, I want the system to automatically detect and alert me to abnormal performance patterns so that I can intervene promptly before issues escalate."

Description

The Intelligent Anomaly Detection requirement leverages AI-driven analysis to automatically identify subtle deviations and unusual patterns within sensor and performance data. By comparing real-time inputs with historical trends, this feature minimizes false alarms and prioritizes genuine operational concerns, reducing downtime and optimizing resource allocation.

Acceptance Criteria

Real-Time Anomaly Identification

Given real-time sensor data input, when the Intelligent Anomaly Detection system processes the information, then any detected deviations from the historical norms must be flagged within 1 second.

Historical Trend Comparison

Given access to historical performance data, when the system compares current sensor readings with past trends, then anomalies must be identified only if deviations exceed defined thresholds, reducing false positives by at least 50%.

Dynamic Threshold Adjustment

Given variable operational conditions, when new environmental or user-input data is received, then the system shall automatically adjust anomaly detection thresholds to maintain accuracy in alerts.

False Alarm Reduction

Given noisy sensor data, when the Intelligent Anomaly Detection algorithm assesses the incoming information, then it must filter out transient fluctuations, ensuring a false alarm rate below 10% during prolonged operation.

Alert History and Analytics Dashboard

"As an operations analyst, I want to review historical alert data and trends so that I can identify recurring issues and fine-tune system parameters for improved efficiency."

Description

The Alert History and Analytics Dashboard requirement involves developing a comprehensive dashboard to log all triggered alerts alongside contextual data. This feature allows users to analyze historical alert trends, refine threshold settings, and generate actionable insights for continuous improvement of system performance.

Acceptance Criteria

Real-Time Alert Capture

Given the dashboard is active, when an alert is triggered by the system, then the alert is logged in real-time with a timestamp and associated contextual data.

Historical Trend Analysis

Given that historical data is available, when a user selects a specific time frame, then the dashboard displays a graphical summary of alert trends and counts for that period.

Customizable Alert Filtering

Given multiple alert categories are recorded, when a user applies filter criteria, then the dashboard updates to show only alerts matching the user-defined conditions and allows for exporting the filtered data.

Custom Dashboards

This feature enables users to personalize their analytics interface, tailoring the displayed metrics to their specific roles and needs. It enhances user engagement and ensures that critical insights are always at the forefront for decision-makers.

Requirements

Dynamic Widget Configuration

"As an operations manager, I want to configure dashboard widgets so that I can monitor the metrics most critical to my role quickly."

Description

Allow users to add, remove, and reposition various data widgets on their dashboard interface. This functionality includes a user-friendly drag-and-drop mechanism along with widget-specific configuration options, ensuring that each role-specific metric is easily accessible and updatable in real-time.

Acceptance Criteria

Widget Addition via Drag-and-Drop

Given a blank dashboard, when a user drags a widget from the library onto the dashboard, then the widget is added with its default settings and available for further customization.

Widget Removal Functionality

Given a dashboard with configured widgets, when a user selects a widget and clicks its 'remove' action, then the widget is immediately removed and the dashboard is updated accordingly.

Widget Repositioning Feature

Given a populated dashboard, when a user drags and drops a widget to a new location, then the widget repositions seamlessly and the overall layout adjusts dynamically without errors.

Widget Specific Configuration Options

Given a widget on the dashboard, when a user accesses the widget's configuration options, then the system displays all widget-specific settings allowing real-time parameter adjustments with instant preview.

Real-Time Widget Data Updates

Given an active dashboard with data widgets, when live data is received, then the widget refreshes automatically to present the updated data in real-time without manual intervention.

Real-Time Data Integration

"As an engineer, I want to see real-time data on my dashboard so that I can respond immediately to any operational anomalies."

Description

Seamlessly integrate a live data feed for dashboard metrics, merging inputs from AI-driven monitoring systems with real-time operational data. This ensures that decision-makers have immediate access to the most current information for swift operational adjustments, reducing downtime and costs.

Acceptance Criteria

Live Data Feed Initialization

Given the dashboard loads with an active network connection, when the system initializes the live data feed, then the dashboard must display the first set of real-time metrics within 2 seconds.

Seamless Data Merging

Given that both AI-driven monitoring and real-time operational systems are active, when data from these sources is merged, then the dashboard must show a unified and error-free view of metrics.

Real-Time Alert Notification

Given that the integrated real-time feed is in operation, when critical service thresholds are breached, then the system should trigger and display immediate alerts on the custom dashboard.

Data Accuracy Verification

Given the data integration from multiple sources, when the metrics are processed and displayed, then they must reflect accurate values within a 1% margin of error.

Dashboard Refresh Consistency

Given the ongoing data feed updates, when the custom dashboard refreshes, then all displayed metrics should update smoothly without lag or inconsistencies.

Customizable Metric Filters

"As a field operator, I want to set custom filters on my dashboard metrics so that I can highlight critical data and receive tailored insights."

Description

Enable users to apply personalized filters and thresholds on displayed metrics, allowing them to focus on critical data points. This functionality supports deep dive analysis and tailored data visualization, enhancing the overall decision-making process with user-specific insights.

Acceptance Criteria

User Filter Application

Given a user is on the ReservoirSnap dashboard, when they select a custom filter for metric display, then the system should apply the filter and update the metrics in real-time.

Threshold Customization Accuracy

Given a user sets personalized threshold values for specific metrics, when these thresholds are reached or breached, then the system should trigger alerts and visually highlight the affected metrics.

Persistence of User Settings

Given a user configures custom filters on their dashboard, when the user logs out and logs back in, then the previously saved filter settings should be automatically restored.

Responsive Filter UI

Given a user accesses the dashboard on multiple devices, when they interact with the filter options, then the filtering interface should respond instantly and display confirmations without lag.

Error Handling for Filter Application

Given a user applies a filter and encounters a connectivity or backend issue, when the operation fails, then the system should display a clear error message with recommended remedial steps.

Role-Based Dashboard Templates

"As an oilfield manager, I want to start with a dashboard template tailored to my role so that I can quickly access relevant insights without spending time on manual configuration."

Description

Provide a set of pre-configured dashboard templates that cater to the specific needs of different user roles in oil and gas operations. These templates offer a jump-start in dashboard setup, ensuring that essential metrics and insights are prioritized for each role, with further customization available as needed.

Acceptance Criteria

Operator Dashboard Initialization

Given a user logged in as a Field Operator, when they navigate to the custom dashboards, then the system should automatically load a pre-configured template displaying operator-specific metrics such as equipment uptime and maintenance alerts.

Engineer Dashboard Initialization

Given a user logged in as an Engineer, when they access the custom dashboards, then the system should present a template emphasizing technical metrics like production rates, sensor status, and drill performance.

Admin Dashboard Configuration

Given a user logged in as an Administrator, when they choose the dashboard customization option, then the system should provide a template integrating operational alerts, financial metrics, and overall system health indicators.

Customization Flexibility

Given a user with access to any role-based dashboard template, when they initiate customization, then the system should allow adding, modifying, or removing widgets without compromising the essential role-specific metrics.

Template Save and Recall

Given a user customizes a pre-configured template, when they save the dashboard configuration, then the system should persist the custom settings and allow retrieval in subsequent sessions.

Interactive Data Visualization Tools

"As an analytics specialist, I want interactive visualization tools on my dashboard so that I can explore data trends and make data-driven decisions effectively."

Description

Incorporate advanced, interactive visualization tools that offer drill-down capabilities and dynamic charting options. This empowers users to explore trends and understand correlations in the data, promoting a more proactive approach to monitoring and decision-making in field operations.

Acceptance Criteria

Drill Down Analysis Scenario

Given that a visual chart is displayed on the dashboard, when a user clicks on a specific data point, then the system shall display a detailed breakdown with further drill-down options.

Dynamic Chart Update Scenario

Given that the user applies a filter to the dataset, when the filter action is executed, then the system shall update the charts and graphs dynamically within 5 seconds with accurate recalculations.

Interactive Hover Information Scenario

Given that a user hovers over a chart element, when the hover event is triggered, then the system shall display a tooltip with key metrics and trend analysis details.

Customization Save and Load Scenario

Given that a user has customized their dashboard layout and visualizations, when the user clicks on the save button, then the configuration shall be stored and reliably reloaded in subsequent sessions.

Responsive Visualization Rendering Scenario

Given that a user accesses the dashboard from a mobile device, when the dashboard loads, then the system shall render interactive visualizations correctly and responsively across different screen sizes.

Eco Sensor Array

Deploy advanced sensors across field operations to capture real-time data on air quality, temperature, humidity, and particulate matter. This feature provides immediate insights into environmental conditions, empowering field operators and production managers to monitor ecological metrics and ensure sustainable practices.

Requirements

Real-Time Environmental Monitoring

"As a field operator, I want to view real-time environmental data so that I can quickly adjust operations based on current conditions and maintain a safe and sustainable work environment."

Description

This requirement facilitates the continuous collection and display of environmental data including air quality, temperature, humidity, and particulate matter from advanced sensors deployed in the field. It ensures that real-time metrics are available for immediate operational review, enhancing the ability to detect and respond to environmental changes, thus improving safety, sustainability, and operational efficiency.

Acceptance Criteria

Real-Time Data Acquisition

Given sensors are deployed in the field, when the environmental monitoring system is activated, then real-time data for air quality, temperature, humidity, and particulate matter must be captured and displayed within 2 seconds.

Accurate Environmental Alerts

Given predefined environmental thresholds, when sensor data exceeds acceptable limits, then an immediate alert must be triggered and communicated via the system notification dashboard.

Dashboard Visualization

Given the availability of real-time sensor data, when a production manager accesses the ReservoirSnap dashboard, then the environmental metrics should display accurate and live-updated visualizations at regular intervals.

Historical Data Storage

Given continuous sensor output, when environmental data is captured, then historical logs must be maintained for a minimum of 30 days with accessible query and trend analysis capabilities.

System Performance and Reliability

Given a significant influx of sensor data during peak operational periods, when the system processes this information, then it must update environmental metrics within 3 seconds to ensure continuous reliability.

AI-Driven Data Insights

"As a production manager, I want to receive AI-driven insights from the sensor data so that I can anticipate potential issues and implement preventive measures effectively."

Description

This requirement involves the processing and analysis of collected sensor data using advanced AI algorithms to generate predictive insights and trend analysis. The integration of AI enhances decision-making by identifying anomalies, forecasting environmental risks, and providing actionable recommendations, thereby supporting proactive management and operational adjustments.

Acceptance Criteria

Real-Time Anomaly Detection

Given real-time sensor data inputs, when an anomaly is detected that exceeds predefined thresholds, then the system must trigger an immediate alert and log the anomalous data for further analysis.

Predictive Environmental Risk Forecasting

Given historical and current sensor data, when analyzed by the AI algorithm, then the system should generate predictive risk scores with a minimum accuracy rate of 90% on test datasets.

Actionable Recommendation Generation

Given trend analysis of sensor data, when deviations from normal patterns are detected, then the system must provide specific, actionable recommendations for operational adjustments.

Data Trend Analysis Accuracy

Given continuous sensor data streams, when processed by the AI algorithm, then the system must display trend analysis segmented by no more than one-hour intervals and meet established statistical accuracy benchmarks.

Seamless Data Integration

Given multiple data sources from various sensors, when the data is aggregated by the system, then it should integrate seamlessly into the dashboard with an update frequency of under 2 minutes post receiving data.

Sensor Calibration and Maintenance Alerts

"As an engineer, I want to receive automated alerts for sensor calibration and maintenance so that I can ensure the sensors are functioning correctly and producing reliable data."

Description

This requirement provides an automated system for monitoring sensor calibration and health, sending alerts when maintenance or recalibration is required. It underpins the reliability and accuracy of the sensor data by ensuring that all sensors operate within their specified parameters, thereby improving data quality and operational consistency.

Acceptance Criteria

Real-time Sensor Calibration Monitoring

Given sensor data is received in real-time, when the calibration deviation exceeds the defined threshold, then the system triggers an immediate recalibration alert to field operators.

Automated Sensor Health Diagnostics

Given sensors are continuously monitored, when AI detects prolonged deviations from baseline sensor readings, then the system should automatically send a maintenance alert with diagnostic details.

Scheduled Sensor Recalibration Alert

Given a predefined calibration schedule, when a sensor's scheduled recalibration time is reached, then the system automatically sends a timely reminder alert to the maintenance team.

Alert Delivery Acknowledgement

Given an alert has been generated, when a field operator receives it, then the system must log an acknowledgement to confirm successful communication.

Sensor Data Quality Preservation

Given maintenance actions are performed based on alerts, when a follow-up calibration test is executed, then the sensor data quality should meet or exceed the acceptable performance parameters.

Dashboard Integration for Eco Sensor Data

"As an operations manager, I want to see integrated environmental and production data on the ReservoirSnap dashboard so that I have a complete overview of operational conditions in one place."

Description

This requirement involves integrating the sensor data directly into the ReservoirSnap dashboard. It aligns environmental metrics with existing operational data, offering a unified view that supports holistic decision-making. The integration enhances usability by providing a seamless interface for monitoring both production and environmental variables, leading to more informed and timely operational decisions.

Acceptance Criteria

Real-Time Data Integration

Given that the Eco Sensor Array collects live environmental data, when data is received, then the dashboard must display real-time updates within a 5-second refresh interval.

Unified Metrics View

Given existing production data and incoming sensor data, when both data feeds are integrated, then the dashboard must present a unified view aligning key production and environmental metrics.

Data Accuracy and Consistency Verification

Given that sensor data inputs are critical, when the data is fetched from sensor feeds, then the dashboard must validate and display data with at least 99% accuracy compared to the raw input.

User Interaction and Filtering

Given that users need to focus on specific environmental metrics, when a filter is applied, then the dashboard must update to show only the selected metric's data within 3 seconds.

Alert and Notification Integration

Given that the dashboard integrates both production and environmental data, when sensor readings exceed predefined thresholds, then the system must trigger alerts and notifications promptly on the UI.

Impact Analyzer

Utilize AI-driven analytics to evaluate collected environmental data, identify trends, and predict potential field impact. The Impact Analyzer helps organizations proactively address ecological risks, optimize resource use, and support environmentally sustainable operations.

Requirements

Real-Time Data Ingestion

"As an engineer, I want the Impact Analyzer to process environmental data as it arrives so that I can react promptly to emerging risks."

Description

Enable the Impact Analyzer to receive and process environmental sensor data in real-time, ensuring that analyses and alerts are generated immediately. This feature minimizes data latency, providing up-to-date insights that drive timely operational decisions and mitigate risks in rapidly changing field conditions.

Acceptance Criteria

Sensor Data Flow Initiation

Given environmental sensor data is generated, When data is ingested by the system, Then the Impact Analyzer must process and validate the incoming data within 2 seconds of arrival.

Continuous Real-Time Monitoring

Given the Impact Analyzer is active, When receiving a continuous stream of sensor data, Then the system must update analysis metrics in real-time and trigger alerts instantly if data anomalies are detected.

Scalability Under High Data Volumes

Given simultaneous data streams from multiple sensors, When operating under peak load conditions, Then the system must maintain data ingestion latency below a defined threshold without any backlog.

Data Accuracy Verification

Given real-time environmental sensor input, When the data is ingested, Then the system must perform accuracy checks and ensure data integrity is maintained before forwarding data for analysis.

Environmental Trend Prediction

"As a field operator, I want the system to analyze trends in environmental data so that I can proactively address potential impacts."

Description

Implement AI-driven algorithms that analyze historical and current environmental data to identify trends and forecast potential field impacts. This feature will help operators anticipate challenges, optimize resource allocation, and implement proactive measures to reduce ecological and operational risks.

Acceptance Criteria

Real-Time Data Integration

Given a live feed of environmental data, when the system processes the incoming data, then the algorithm must analyze and display predictive trends within 2 seconds.

Historical Data Analysis Capability

Given a historical dataset containing at least 12 months of environmental data, when the algorithm runs, then it shall accurately identify trends with a 95% correlation to verified manual analysis.

Proactive Impact Notification

Given predefined ecological risk thresholds, when the algorithm forecasts a potential field impact that exceeds these thresholds, then the system shall generate notifications for operators within 1 minute.

Automated Resource Adjustment Suggestion

Given forecasted environmental trends indicating potential operational risks, when the algorithm computes necessary adjustments, then it shall suggest optimal resource allocation changes with actionable intelligence.

Impact Visualization Dashboard

"As an operations manager, I want a clear visualization of predicted impacts so that I can easily understand complex data and respond effectively."

Description

Develop an intuitive, interactive dashboard that presents real-time analytics and predictions using charts, graphs, and customizable views. This interface will simplify complex data, enabling users to quickly interpret key insights and make informed decisions to maintain operational efficiency and environmental safety.

Acceptance Criteria

Interactive Visualization Launch

Given the Impact Visualization Dashboard is loaded, when a user selects a specific well, then the dashboard must display real-time charts, graphs, and updated environmental data with customizable filters.

Customizable Data Views

Given a user logs into the Impact Visualization Dashboard, when they access the customization settings, then the dashboard must allow the arrangement and saving of widget layouts and reflect these changes within 2 seconds.

AI Prediction Trends

Given continuous environmental data collection, when the AI analytics are activated, then the dashboard must display predictive trends with visual indicators and provide alerts for potential field impacts.

Operational Efficiency Highlight

Given the dashboard is actively monitored during field operations, when a data anomaly is detected, then the dashboard should highlight the affected metric in red and suggest operational adjustments within 1 minute.

Mobile Responsiveness

Given a user accesses the dashboard on a mobile device, when the page loads, then the UI must adapt seamlessly to various screen sizes and orientations, ensuring full functionality under 3 seconds.

Seamless API Integration

"As a system integrator, I want reliable API connectivity so that I can ensure a seamless and secure transfer of environmental data into the Impact Analyzer."

Description

Create robust API connectivity to integrate various external environmental data sources with the Impact Analyzer. This feature is essential for ensuring consistent, secure, and reliable data ingestion, enabling comprehensive analysis by combining diverse datasets to improve prediction accuracy.

Acceptance Criteria

Data Ingestion Validation

Given an external environmental data source is available, when data is transmitted via API, then the system must ingest the data securely and within 2 seconds.

Secure Connection Setup

Given proper API authentication credentials are provided, when an API call is made, then the data transfer must occur over HTTPS with TLS 1.2 or higher.

Data Consistency Check

Given multiple external data sources, when data is integrated into the Impact Analyzer, then the imported data must be normalized and validated to ensure consistency.

Error Handling Mechanism

Given a failure in API communication, when an error occurs, then the system must log the error details and trigger an alert with a built-in retry mechanism.

Scalability Under Load

Given high concurrent data transmission scenarios, when the API is receiving data from multiple sources simultaneously, then the system must sustain at least 100 requests per second without performance degradation.

Green Compliance Tracker

Automatically monitor and compare environmental data against regulatory standards. This feature alerts users to deviations from local and international environmental guidelines, ensuring compliance and reducing field impact while streamlining sustainability processes.

Requirements

Real-time Environmental Data Ingestion

"As an oil and gas field operator, I want to receive real-time environmental data so that I can promptly identify and address any compliance issues."

Description

This requirement ensures the system can continuously ingest environmental data from various sensors and external data sources. The integration will consolidate and normalize data streams in real time, providing a seamless flow of accurate and timely information. This data is core for comparing against regulatory standards enabling swift detection of deviations and proactive compliance management.

Acceptance Criteria

Real-time Data Stream Processing

Given environmental data is transmitted from sensors, when the system receives the data stream, then it should consolidate and normalize the data in real time with latency less than 2 seconds.

Accurate Data Normalization

Given sensor data comes in varying formats, when it is ingested, then the system should normalize the data into a consistent format with 99.9% accuracy.

Integration with External Data Sources

Given the system is configured with multiple external data endpoints, when data ingestion is initiated, then it should successfully integrate and assimilate data streams in real-time from all sources.

Regulatory Compliance Alert Trigger

Given normalized environmental data is compared against regulatory standards, when a deviation is detected, then the system should trigger an immediate alert to the compliance tracking module.

High-Throughput Load Handling

Given peak operational conditions with high data volume, when real-time data ingestion occurs, then the system must handle at least 10,000 data points per second without data loss.

Automated Regulation Comparison Engine

"As an engineer responsible for compliance, I want automated comparisons to regulatory standards so that I can quickly identify non-compliant data and respond appropriately."

Description

This requirement involves building a robust engine that automatically compares the ingested environmental data against a dynamic set of local and international regulatory standards. It provides automated assessments and flags any discrepancies between observed data and compliance thresholds. This enables quicker evaluations and streamlined reporting processes.

Acceptance Criteria

Real-Time Data Comparison

Given environmental data is ingested into the system, when the Automated Regulation Comparison Engine processes the data, then it should compare the data in real-time against both local and international regulatory standards.

Dynamic Regulatory Update Verification

Given a change in the regulatory standards database, when the engine re-runs the comparison process, then it should automatically update the comparisons to reflect the latest standards without manual intervention.

Discrepancy Alerting Mechanism

Given a scenario where environmental data does not meet regulatory thresholds, when the Automated Regulation Comparison Engine identifies a discrepancy, then it should flag the data and trigger an alert to the user interface for immediate review.

Automated Non-Compliance Reporting

Given that discrepancies are identified, when the engine processes the data, then it should generate a detailed report outlining non-compliance issues and provide recommendations for corrective action, ensuring the report is downloadable and shareable.

Alerts and Notifications System

"As a field operations manager, I want to be alerted immediately when environmental data deviates from compliance so that I can initiate corrective actions without delay."

Description

This requirement mandates the creation of an intelligent notification system that sends real-time alerts when detected data deviates from environmental guidelines. Users will receive these alerts via multiple channels such as SMS, email, or in-app notifications. This system is designed for prompt operational adjustments and enhanced compliance monitoring.

Acceptance Criteria

Real-Time Violation Alert

Given that sensor data indicates deviation from environmental guidelines, when the system verifies a threshold breach, then an immediate alert is sent via SMS, email, and in-app notifications.

Multi-channel Notification Delivery

Given a triggered environmental anomaly alert, when the notification process is activated, then the alert must be delivered successfully across all configured channels including SMS, email, and in-app notifications.

Compliance Deviation Log Tracking

Given an alert is generated due to a detected deviation, when the alert is dispatched, then a detailed log entry must be created capturing timestamp, sensor data values, and alert channel information.

User Preference Notification Settings

Given a user has defined custom alert settings, when environmental data exceeds these personalized thresholds, then the system should send notifications as per the user's configured preferences for channel and frequency.

High-Frequency Data Trigger Handling

Given that the sensor data stream continuously monitors environmental metrics, when multiple rapid deviations are detected, then the system must aggregate alerts to prevent notification spamming while ensuring timely compliance actions.

Compliance Reporting Dashboard

"As a compliance auditor, I want to access a dashboard showing historical and current compliance data so that I can easily track performance and verify adherence to regulations."

Description

This requirement includes the development of a comprehensive dashboard that displays environmental compliance metrics. The dashboard aggregates and visualizes historical data, current status, and trends over time. It is intended to provide users with clear insights and facilitate compliance audits and decision-making.

Acceptance Criteria

Real-Time Environmental Metrics

Given the dashboard is loaded, when real-time data is received from sensors, then the dashboard must display updated compliance metrics within 5 seconds.

Historical Trends Dashboard

Given the dashboard is accessed, when a user selects a historical date range, then the dashboard shall display aggregated environmental data and trend charts for that period.

Compliance Alerts & Notifications

Given predefined environmental thresholds, when current compliance data deviates from regulatory standards, then the dashboard should trigger an alert displaying the deviation details.

User Interactivity & Drilldown

Given an interactive visual element on the dashboard, when a user clicks on a specific data point, then the dashboard must provide a detailed drill-down view with supporting metrics.

Exporting Compliance Reports

Given the displayed compliance metrics, when a user selects the export option, then a downloadable PDF report aggregating historical and current compliance data is generated.

Data Normalization and Quality Assurance

"As a data analyst, I want environmental data to be standardized and quality checked so that the compliance comparisons are based on reliable information."

Description

This requirement ensures that all incoming environmental data is cleansed, normalized, and validated before comparison against compliance standards. It includes procedures for handling data anomalies and ensuring consistency across diverse data sources. The aim is to minimize errors and enhance the accuracy of compliance assessments.

Acceptance Criteria

Data Ingestion Cleanliness

Given raw environmental data from varied sources, when the system ingests the data, then it must cleanse, deduplicate, and normalize the data to a consistent schema before further processing.

Real-time Anomaly Detection

Given cleansed and normalized data, when anomalies such as missing values, out-of-bound figures, or format discrepancies occur, then the system should detect and flag these anomalies while logging them for review.

Compliance Accuracy Validation

Given a dataset that has passed quality checks, when it is compared against environmental regulatory standards, then the system must ensure the data meets predefined accuracy thresholds and trigger alerts for any deviations.

Sustainable Insights Dashboard

An intuitive dashboard that consolidates all environmental metrics and analytics into clear, actionable insights. By visualizing historical trends and real-time data, users can make informed decisions that enhance production efficiency while prioritizing ecological balance.

Requirements

Intuitive Data Visualization

"As an oil and gas field operator, I want to see interactive charts and graphs so that I can readily interpret environmental data and make informed decisions about production adjustments."

Description

Integrate interactive and visually engaging graphs and charts that display historical and real-time environmental metrics. This feature allows users to quickly interpret complex data sets, identify trends, and pinpoint potential issues that might affect operational efficiency, ensuring timely decision-making and compliance with environmental guidelines.

Acceptance Criteria

Real-Time Metrics Visualization

Given a logged-in user accesses the Sustainable Insights Dashboard, when the dashboard loads, then real-time graphs display environmental metrics updated every 30 seconds.

Historical Data Trends Analysis

Given a user selects a specific historical date range, when the dashboard processes the request, then the graphs accurately display historical trends with appropriate time filters.

Interactive Graph Components

Given a user hovers over any interactive graph element, when action is performed, then a tooltip with detailed environmental metrics appears and is easily readable.

Compliance with Environmental Guidelines

Given a potential threshold breach in real-time environmental data, when the graphs detect such anomalies, then an alert is automatically generated to notify the user.

Graph Customization and Scaling

Given a user accesses settings for visualization, when customization options are applied, then the dashboard reflects changes immediately by updating graph types, color schemes, and scales accordingly.

Real-time Alerts and Notifications

"As an engineer, I need to receive real-time notifications when critical metrics deviate so that I can address potential issues before they impact production."

Description

Implement a real-time alert system that notifies users when environmental metrics deviate from predefined thresholds. The system ensures that operators receive immediate notifications, enabling them to take corrective actions promptly and maintain optimal production conditions while adhering to sustainability standards.

Acceptance Criteria

Threshold Breach Alert Activation

Given environmental metrics are being continuously monitored, When a metric value exceeds its predefined threshold, Then a real-time alert is triggered and displayed to the user.

Delayed Notification Recovery

Given a triggered alert remains unacknowledged, When the system detects the alert has not been confirmed for a set duration, Then the system resends the notification to the user.

Alert Notification Delivery Performance

Given an alert condition is met, When the alert is triggered, Then the notification is delivered to the designated endpoint within 2 seconds.

Mobile and Desktop Consistency

Given an alert notification is generated, When displayed on both mobile and desktop interfaces, Then the alert maintains consistent formatting, content, and timing.

User Acknowledgement Logging

Given a user receives an alert notification, When the user acknowledges the alert, Then the acknowledgement is logged with a timestamp in the system database.

Historical Data Analysis

"As a field operator, I want to analyze historical environmental data so that I can understand trend patterns and predict future challenges."

Description

Develop a module that aggregates historical data and applies AI-driven analytics to identify patterns and trends over time. This analytical tool supports predictive maintenance and proactive adjustments by highlighting anomalies and forecasting environmental risks that could affect production efficiency and sustainability.

Acceptance Criteria

Real-time Historical Analysis Module

Given a complete dataset of historical data, When the module aggregates and applies AI analytics, Then the system should identify patterns and trends with 95% accuracy.

Anomaly Detection in Historical Data

Given a dataset containing known anomalies, When the module processes this dataset, Then it should highlight anomalies and trigger an alert with a false positive rate below 5%.

Predictive Maintenance Forecasting

Given historical operational metrics, When the analysis is performed, Then the module must forecast maintenance needs accurately, as validated by domain experts.

Environmental Risk Forecasting

Given historical environmental metrics, When the AI-driven analytics are applied, Then the module should forecast environmental risks that affect production efficiency and provide actionable recommendations.

Customizable Dashboard Layout

"As an oil and gas professional, I want a customizable dashboard so that I can easily access the environmental metrics that matter most to my operations."

Description

Allow users to customize the layout of the dashboard to prioritize metrics and analytics most relevant to their operations. This flexibility enhances user experience by enabling personalized views that align with specific operational priorities, ensuring swift access to the most critical data.

Acceptance Criteria

User Initiates Custom Layout Setup

Given a logged-in user on the dashboard, when the user selects 'Customize Layout', then the system should display the layout editing interface.

Drag and Drop Ordering

Given the layout editor is open, when a user drags a metric block to a new position, then the change should be saved and immediately reflected on the dashboard.

Responsive Layout on Mobile Devices

Given a user accesses the dashboard on a mobile device, when the user views their customized layout, then the layout must adjust responsively and display correctly on various screen sizes.

Save and Reset Preference

Given a customized layout, when the user clicks 'Save', then the new layout should persist across sessions; when the user clicks 'Reset', then the layout should revert to default settings.

Access Control Customization

Given a multi-role user environment, when a user with limited permissions customizes their dashboard, then they can only modify elements permitted for their role and the changes should not affect other users.

Export and Reporting Feature

"As a production manager, I want to export dashboard reports in different formats so that I can share detailed analytics with stakeholders and regulators."

Description

Integrate export functionalities that enable users to download comprehensive reports of environmental analytics in various formats. This feature supports compliance and operational audits by providing detailed documentation and facilitating data sharing with regulatory bodies and management teams.

Acceptance Criteria

Real-Time Report Generation

Given the user is on the Sustainable Insights Dashboard, When the user selects a time range and clicks the 'Export' button, Then a comprehensive report including all relevant environmental metrics is generated in real-time.

Multi-Format Report Export

Given the report is generated, When the user selects a preferred format (PDF, CSV, XLSX) from the export options, Then the report is successfully downloaded in the selected format with complete data integrity.

Compliance and Audit Report Detail

Given the user accesses the export feature for compliance needs, When the system compiles regulatory fields with environmental analytics data, Then the exported report conforms to audit and compliance standards.

Error Handling During Export

Given the user initiates the export process, When a system error or data inconsistency is detected, Then the system displays an appropriate error message and halts the export process to prevent incomplete report generation.

Eco Alert System

Integrate customizable, real-time notifications that trigger when environmental thresholds are breached. This feature ensures timely responses to emerging ecological issues, minimizes environmental risks, and facilitates rapid intervention, safeguarding both operations and the ecosystem.

Requirements

Threshold Breach Detection

"As an oil field operator, I want the system to automatically detect when an environmental metric breaches a predefined threshold so that I can take immediate remediation actions."

Description

Implement sensor monitoring logic that compares environmental readings with predefined thresholds using AI-driven analytics to detect anomalies in real time. The system should trigger alerts as soon as any environmental metric exceeds safe limits, providing a first line of defense against ecological hazards.

Acceptance Criteria

Real-time Anomaly Alerting

Given a sensor is monitoring environmental readings, when a metric exceeds its predefined threshold, then generate an alert through the Eco Alert System within 5 seconds, including metric value and breach details.

Consistent Trending Analysis

Given historical environmental data readings and established thresholds, when the system processes and analyzes the data, then it must accurately identify trends indicating potential future breaches with a minimum accuracy of 90%.

User Acknowledgement of Alerts

Given an alert is generated for a breached threshold, when the notification is delivered to the operator, then the system must offer an option for the operator to acknowledge the alert and record their response on the interface.

Custom Alert Configuration

"As an engineer, I want to customize alert thresholds so that the notifications align with my operational requirements and unique environmental conditions."

Description

Develop a flexible user interface that allows users to set and customize environmental parameters and alert levels according to operational needs. The interface will help tailor alerts, ensuring notifications are relevant and actionable, and integrate with the main ReservoirSnap dashboard.

Acceptance Criteria

User Custom Alert Threshold Setup

Given the user is on the Custom Alert Configuration interface, When the user selects environmental parameters and inputs threshold values, Then the system must validate and save these values, reflecting them in the ReservoirSnap dashboard.

User Custom Alert Level Modification

Given the user is logged into ReservoirSnap, When the user modifies existing alert levels or priorities, Then the system must update the alert settings in real-time and integrate changes with the Eco Alert System.

Real-Time Notification Integration

Given environmental thresholds are breached, When the system detects a violation, Then a real-time notification must be triggered both within the ReservoirSnap dashboard and through designated alert channels.

User Input Validation and Error Handling

Given the user attempts to input invalid environmental parameters, When the interface detects the error, Then an appropriate error message must be displayed and the invalid configuration should not be saved.

Real-time Notification Delivery

"As an operator, I want to receive real-time notifications by my preferred medium so that I can quickly address any environmental issue."

Description

Build a robust notification engine capable of delivering alerts instantaneously across multiple channels such as SMS, email, and in-app notifications. The system’s architecture should ensure low latency and high reliability even under heavy sensor data loads.

Acceptance Criteria

Multi-Channel Notification Dispatch

Given sensor data triggers an environmental threshold breach, when the notification engine processes the alert, then SMS, email, and in-app notifications must be delivered within 3 seconds.

Low Latency Performance

Given a high volume of sensor data, when a threshold breach occurs, then the notification system must maintain a latency of less than 3 seconds for each notification channel.

Reliability under High Load

Given multiple concurrent sensor alerts, when notifications are triggered, then the system must achieve a minimum delivery success rate of 99% across all notification channels.

Scalability in Multi-Region Deployment

Given simultaneous environmental threshold breaches in different regions, when notifications are dispatched, then the system must efficiently route and deliver alerts to all specified channels without delay.

Customizable Notification Settings

Given user-defined notification preferences, when a notification is generated, then alerts must be customized and delivered according to the user's specified channel settings.

Historical Data Analysis Integration

"As an engineer, I want the system to analyze historical trends for environmental data so that I can understand anomalies in context and avoid false alarms."

Description

Incorporate historical environmental data analysis to provide context for alerts. This feature analyzes historical trends to reduce false positives by cross-referencing current sensor data against baseline historical performance. Integration will enhance decision making and mitigate unnecessary alerts.

Acceptance Criteria

Historical Data Baseline Analysis

Given the system has access to historical sensor data, when new real-time sensor data is received, then the system must cross-reference it with historical trends with an accuracy rate of at least 95%.

False Positive Reduction Check

Given that environmental sensors trigger an alert, when the system analyzes the historical trends, then it must reduce false positives by at least 50% compared to when historical data is not used.

Alert Contextualization Integration

Given an alert is generated, when historical data analysis is applied, then the alert must include contextual historical insights within 3 seconds, verified across a sample of at least 5 alerts.

System Performance under Load

Given the system processes both real-time and historical data concurrently, when historical data integration is enabled, then the system must maintain a response time under 2 seconds per transaction under standard load conditions.

User Interface Historical Data Display

Given a user requests historical context for an alert, when the historical data analysis module returns results, then the UI must clearly display at least three key historical data points and visual indicators in an intuitive layout.

Dashboard Notification Center

"As an operator, I want to have a centralized view of all environmental alerts so that I can efficiently monitor and respond to notifications."

Description

Integrate a central dashboard module within ReservoirSnap that consolidates all alerts and notifications. This component should offer filtering, sorting, and detailed views of alerts, enabling users to review notification history and respond effectively to emerging ecological issues.

Acceptance Criteria

Filter and Sort Notifications

Given a logged in user with Dashboard Notification Center open, when new environmental notifications are received, then the system must allow filtering by type, severity, and date, and enable sorting in both ascending and descending order.

Detailed Notification View

Given that the user selects a notification from the Dashboard Notification Center, when the notification is clicked, then the system shall display its complete details including timestamp, type, severity, and additional context data.

Real-Time Notification Updates

Given the Dashboard Notification Center is active, when the Eco Alert System triggers an alert, then the notification must appear on the dashboard within 2 seconds of the event.

Historical Notification Review

Given that the user accesses the Notification History section, when reviewing past notifications, then the system shall display a paginated list sorted by date with options for search and filtering.

Product Details

Vision & Mission

Problem & Solution

Details & Audience

User Personas

Cautious Carla

Background

Needs & Pain Points

Needs

Pain Points

Psychographics

Channels

Innovative Ian

Background

Needs & Pain Points

Needs

Pain Points

Psychographics

Channels

Green Guardian Gina

Background

Needs & Pain Points

Needs

Pain Points

Psychographics

Channels

Product Features

Fingerprint Verify

Requirements

Fingerprint Enrollment Setup

Description

Acceptance Criteria

Real-Time Fingerprint Verification

Description

Acceptance Criteria

Fallback Authentication Mechanism

Description

Acceptance Criteria

Secure Biometric Data Storage

Description

Acceptance Criteria

Token Guardian

Requirements

Dynamic One-Time Token Generation

Description

Acceptance Criteria

Automated Token Expiry and Refresh

Description

Acceptance Criteria

Real-time Token Activity Monitoring

Description

Acceptance Criteria

User-friendly Token Delivery Interface

Description

Acceptance Criteria

Adaptive Access

Requirements

Contextual Risk Analysis

Description

Acceptance Criteria

Dynamic Authentication Workflow

Description

Acceptance Criteria

User Behavior Analytics Dashboard

Description

Acceptance Criteria

Secure Session Manager

Requirements

Real-Time Session Monitoring

Description

Acceptance Criteria

Automated Session Expiration

Description

Acceptance Criteria

Suspicious Activity Alerts

Description

Acceptance Criteria

Immediate Session Termination

Description