ESP Failure Analysis Using Encoded Time-Series ML Models
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Solution Overview
Problem
ESP failure analysis is a tedious, human-intensive, and time-consuming process due to the complexity of operating conditions and the need for real-time monitoring and pattern recognition of high-frequency time-series data, which lacks an efficient and automated solution.
Innovation Solution
A method utilizing machine learning models trained with encoded features from ESP data, including DIFA tracker data, well tracker data, and DIFA reports, using a data engineering pipeline, feature engineering, and an ensemble of machine learning algorithms to automate ESP failure analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signal processing and pattern recognition are used for high-frequency time-series data, then failure detection and prediction capability is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent extracts and selects only the most relevant features from the high-frequency time-series data using feature selection techniques. This reduces the dimensionality of the data while maintaining the essential information needed for failure detection, thereby decreasing processing time without sacrificing predictive accuracy.
Solution Approach 2:
The system performs preliminary data preprocessing, feature extraction, and model training in advance before actual failure detection is needed. Historical data is used to pre-train machine learning models and establish baseline patterns, enabling faster real-time detection when failures occur.
2Measurement precision
If more data is collected at an accelerating pace, then failure analysis accuracy is improved, but the complexity and time-consuming nature of analysis increases
Solution Approach 1:
The patent segments the failure analysis process into distinct modules: data collection, data preprocessing, feature extraction, model training, and failure detection. This modular approach allows each component to be optimized independently and reduces the overall complexity by dividing the large-scale analysis task into manageable segments.
Solution Approach 2:
The system introduces intermediate representation layers between raw data and final failure detection decisions. Feature vectors and encoded representations serve as intermediaries that simplify the relationship between complex raw data and failure patterns, making the analysis more manageable and less computationally intensive.
3Reliability
If manual dismantling and inspection is performed to identify root causes, then failure analysis thoroughness is improved, but time consumption and human resource requirements increase
Solution Approach 1:
The patent replaces manual mechanical inspection with automated digital analysis systems. Machine learning models process sensor data and failure records to automatically identify root causes, substituting human inspectors with computational algorithms that can continuously analyze data without fatigue or bias.
Solution Approach 2:
The system enables self-service failure analysis where the ESP monitoring system automatically detects anomalies, triggers investigations, and generates failure reports without requiring constant human intervention. The system serves itself by using historical data to automatically learn failure patterns and provide diagnostic recommendations.
Data Source
AI summary
A method, apparatus, and program product may utilize data associated with one or more electric submersible pumps (ESPs) to train a machine learning model and/or use a machine learning model to perform ESP failure analysis. In addition, one or more features from the data may be encoded into a machine-readable format to facilitate ingestion by the machine learning model.