Petroleum Equipment Anomaly Detection for Failure Forecasting
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Solution Overview
Problem
Existing prognostic health monitoring solutions in the petroleum industry are often equipment-specific, making it difficult to scale with complex and diverse equipment, and are driven by subject matter experts, which can be time-consuming and costly due to equipment failures and downtime.
Innovation Solution
A computer-implemented method using a computing device with a processor to construct an anomaly detection model by obtaining a feature set associated with failures, determining system anomalies using unsupervised clustering or deep learning, and applying fault mode analysis to predict potential failures and optimize maintenance strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subject matter expert-driven prognostic health monitoring solutions are used, then equipment-specific accuracy is improved, but system complexity and scaling difficulty increase
Solution Approach 1:
The patent implements a universal anomaly detection platform that can monitor multiple types of petroleum industry equipment (drill bits, pumps, compressors, etc.) using a single system architecture. The system uses standardized sensor data inputs and unified machine learning models that can be applied across different equipment types, eliminating the need for separate expert-driven solutions for each device while maintaining detection accuracy through adaptable feature engineering and model selection.
2Measurement precision
If subject matter expert-driven solutions are implemented, then detection accuracy is improved, but implementation time and cost increase
Solution Approach 1:
The system automatically performs feature engineering, model selection, and hyperparameter optimization without requiring manual intervention from subject matter experts. The machine learning pipeline self-configures based on the input data characteristics, automatically selecting appropriate anomaly detection algorithms and tuning parameters. This automation eliminates the time-consuming process of expert consultation and manual model development while maintaining or improving detection accuracy through systematic algorithmic approaches.
3Reliability
If equipment-specific monitoring solutions are used, then reliability for specific equipment is improved, but adaptability to diverse equipment decreases
Solution Approach 1:
The system dynamically adapts to different equipment types by automatically adjusting its monitoring parameters, feature extraction methods, and anomaly detection thresholds based on the specific equipment being monitored. The machine learning models are designed to learn equipment-specific patterns from operational data while maintaining a unified framework that can accommodate various petroleum industry equipment. This dynamic adaptability allows the system to maintain high reliability across diverse equipment without requiring separate specialized solutions for each device type.
Data Source
AI summary
Systems and methods for detecting anomalies and predicting failures in petroleum-industry operations, using Machine Learning. Systems and methods are provided for determining system anomalies and individual resource anomalies using trained models and predicting estimated system times to failure based thereon. Features correlated to system failures may be identified in a root cause analysis and used to perform forecasting to determine when a failure is likely to occur. The forecasting may include determining when one or more components may likely meet certain thresholds associated with failures of the components. A protective action may be performed to protect resources associated with the operations, based on determining the length of time until system failure of the operation is estimated to occur.