Well Operation Anomaly Detection Through Event Change Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Detecting anomalous well operations is challenging due to the dynamic nature of well operations and the large amounts of field measurements that need to be analyzed, making it difficult to efficiently monitor and manage numerous wells with limited personnel and time.
Innovation Solution
A system and method for detecting anomalous well operations by analyzing changes in operation characteristics over time, classifying events based on positive, negative, or no changes, and flagging anomalies using a multi-stage event detection and classification model, enabling efficient monitoring and management of wells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If well operations are monitored using traditional methods, then comprehensive coverage of all wells can be achieved, but the complexity of analyzing large amounts of field measurements increases significantly
Solution Approach 1:
The patent segments the monitoring process into distinct operational phases (normal operation, event detection, anomaly detection) and divides the analysis of multiple wells into individual well assessments. This segmentation allows comprehensive monitoring coverage while reducing the complexity of analyzing all field measurements simultaneously by processing them in manageable segments.
Solution Approach 2:
The patent introduces an intermediary processing layer that automatically classifies events into categories (positive change, negative change, no change) and identifies anomalies based on predefined criteria. This intermediary layer simplifies the analysis complexity by providing structured classification rules that reduce the cognitive load on operators while maintaining comprehensive monitoring coverage.
2Measurement precision
If detailed analysis of all field measurements is performed, then detection precision of anomalous operations improves, but the time required for analysis increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-defining event classification criteria and anomaly detection thresholds before actual monitoring occurs. Historical data is used to establish baseline characteristics and standard deviations, which are then used to automatically classify new measurements. This preliminary preparation enables precise anomaly detection without requiring time-consuming analysis during actual monitoring events.
Solution Approach 2:
The system performs self-service through automated event classification and anomaly detection based on predefined rules and statistical criteria. The system automatically determines whether changes in operational parameters represent normal variations, events, or anomalies without requiring manual analysis, thereby maintaining high detection precision while significantly reducing analysis time.
3Difficulty of detecting and measuring
If manual monitoring of multiple wells is increased, then detection capability improves, but personnel requirements and operational costs increase
Solution Approach 1:
The patent implements self-service monitoring where the system automatically detects events and anomalies using predefined classification criteria and statistical thresholds. The system performs self-diagnosis and self-alerting functions, eliminating the need for increased personnel while maintaining or improving detection capability through automated processing of field measurements.
Solution Approach 2:
The patent replaces manual mechanical monitoring processes with automated electronic systems that use computational algorithms to detect and classify events. The mechanical process of manual analysis is substituted with electronic data processing, machine learning models, and automated alerting systems, thereby reducing personnel requirements while enhancing detection capability.
4Speed
If real-time monitoring of all operation characteristics is implemented, then response time to anomalies improves, but the volume of data to be processed increases significantly
Solution Approach 1:
The patent extracts only the critical information from the vast volume of operational data by focusing analysis on predefined key parameters and their changes over time. The system extracts event-level information and anomaly indicators rather than processing every raw data point, thereby maintaining real-time response capability while significantly reducing the effective data volume that requires processing.
Solution Approach 2:
The patent applies partial action by monitoring and analyzing only the most critical operation characteristics and events that indicate potential anomalies, rather than equally processing all possible parameters. This selective monitoring approach enables real-time response to meaningful events while reducing the overall data processing volume by excluding less relevant measurements.
Data Source
AI summary
Events at a well over a duration of time are detected based on changes in the values of the operation characteristics of the well. The changes in values of operation characteristics are classified as being positive change, negative change, or no change, and the events at the well are detected based on combinations of positive change, negative change, and no change for different operation characteristics. Anomalies at the well are detected based on the events detected at the well over the duration of time.