Drilling Equipment Failure Prediction for Replacement Value Balancing
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Solution Overview
Problem
Conventional solutions for predicting equipment failures in oil and gas drilling and recovery operations often assume average equipment behavior, leading to either increased non-productive time and customer dissatisfaction by predicting run-to-failure or unnecessary costs from premature replacement.
Innovation Solution
A machine-learning model enhanced with a customized value analysis and cost function is applied to predict equipment failures, balancing precision and recall accuracy to optimize the remaining useful life of equipment, minimizing non-productive time costs and maximizing equipment usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine-learning model predicts equipment failure well before actual failure, then equipment replacement can be planned in advance, but this results in increased cost from replacing equipment before the end of its useful life
Solution Approach 1:
The patent applies parameter changes by adjusting the prediction threshold and timing parameters in the machine-learning model to optimize the balance between early detection and avoiding premature replacement. By dynamically tuning these parameters based on equipment-specific data and operational context, the system achieves accurate predictions while minimizing unnecessary early replacements.
Solution Approach 2:
The system employs dynamic prediction horizons and adaptive thresholds that adjust based on equipment age, operational conditions, and historical data. This dynamic approach allows the model to predict failures at optimal times rather than using fixed thresholds, thereby reducing premature replacements while maintaining high prediction accuracy.
2Productivity
If a machine-learning model predicts run-to-failure, then equipment can be used to maximum capacity, but this leads to an increase in non-productive time (NPT) cost and customer dissatisfaction
Solution Approach 1:
The system performs preliminary actions by predicting equipment failures before they occur, enabling advance scheduling of maintenance activities. This allows equipment to be taken out of service proactively rather than reactively, reducing unplanned downtime and allowing for better resource allocation during maintenance windows.
Solution Approach 2:
The system implements feedback loops where actual failure data and maintenance outcomes are continuously fed back into the machine-learning model. This feedback mechanism refines prediction accuracy over time and allows the system to learn from past performance, progressively improving its ability to balance equipment utilization with avoidance of non-productive time.
3Device complexity
If conventional solutions assume average behavior of equipment, then analysis is simplified, but this reduces accuracy in predicting individual equipment failures
Solution Approach 1:
The patent applies segmentation by dividing the equipment population into individual units and analyzing each piece of equipment separately rather than as an average group. The machine-learning model processes equipment-specific data independently, capturing unique patterns and behaviors for each asset, thereby achieving high prediction accuracy without requiring overly complex centralized analysis.
Data Source
AI summary
The value for equipment to be replaced can be maximized by determining a threshold cutoff value for a failure prediction indicator and a window size for obtaining the threshold cutoff value for a piece of oil or gas drilling or recovery equipment; applying the threshold cutoff value and the window size to an equipment failure prediction model; and deriving a recall value and an average hour-loss value from the equipment failure prediction model. Predictive maintenance for the piece of oil or gas drilling or recovery equipment may be performed based on the recall value and the average hour-loss value to perform predictive maintenance for a piece of equipment in an oil or gas recovery operation.