Digital Twin Fault Evaluation for Green Energy Well Dynamics
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Solution Overview
Problem
Existing systems face challenges in accurately predicting and detecting system faults and failures in green energy well systems, which are crucial for efficient monitoring and predictive maintenance, due to the complexity of well dynamics and the need for precise signal and process mathematical models.
Innovation Solution
A digital twin system utilizing a physics-based model and machine learning algorithms, specifically an artificial neural network and Naïve Bayes algorithm, to emulate well dynamics behavior, determine impact levels and likelihood of abnormalities, and transmit commands for proactive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a physics-based model is used to emulate well dynamics behavior, then the accuracy of normal operation prediction is improved, but the ability to detect and diagnose faults and failures deteriorates
Solution Approach 1:
The patent combines a physics-based model with a machine learning model into a unified digital twin system. The physics-based model provides accurate predictions of normal well dynamics behavior, while the machine learning model simultaneously learns to detect and diagnose faults by analyzing deviations from normal behavior. This merging allows the system to maintain high prediction accuracy while enhancing fault detection capabilities through the complementary strengths of both modeling approaches.
2Productivity
If the number of well operations increases to maximize production, then the productivity is improved, but the risk of system faults and failures increases
Solution Approach 1:
The digital twin system performs preliminary action by continuously predicting well dynamics behavior and detecting potential faults before they lead to actual system failures. The machine learning model analyzes operational data in real-time, identifying early signs of abnormalities and enabling preventive maintenance actions. This allows the well system to operate at maximum productivity while maintaining high reliability through early fault detection and intervention.
3Reliability
If a digital twin system is implemented for predictive maintenance, then the early detection of faults is improved, but the system complexity increases
Solution Approach 1:
The patent creates a digital twin, which is a virtual copy of the physical well system. This digital replica receives and processes the same operational data as the physical system, allowing fault detection and prediction without adding physical complexity to the actual well infrastructure. The machine learning model operates on this copied data environment, enabling sophisticated analysis while keeping the physical system relatively simple.
Data Source
AI summary
A method of managing a well system includes: obtaining, by a digital twin manager and based on field well dynamics behavior data of the well system, emulated well dynamics behavior data using a physics-based model; obtaining, based on a predetermined monitoring criterion, predicted well dynamics behavior data of the well system using a physics constrained machine learning model that is based on the emulated well dynamics behavior data and the field well dynamics behavior data; determining, using a second machine learning model, an impact level that associates the predicted well dynamics behavior data with a well system abnormality; determining, using the second machine learning model, a likelihood level that associates the predicted well dynamics behavior data with the well system abnormality; determining a probability and a risk level of the well system abnormality based on the impact level and the likelihood level.