AI Bit Wear Model Training via Physics-Guided Simulation
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Solution Overview
Problem
Conventional bit wear models for earth-boring tools face challenges in accurately predicting wear due to limited and noisy field data, leading to inefficient drilling operations and premature tool failure, as they struggle to account for physical realities of wear over distance and time.
Innovation Solution
The earth-boring tool system combines field data with a physics-based bit wear model to generate a more robust wear prediction model, using sensors and machine learning to train an AI bit wear model that optimizes drilling parameters and extends tool lifespan by accurately simulating wear progression versus drilling depth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional bit wear models use limited field data for training, then model training is simpler and faster, but prediction accuracy deteriorates due to noisy and insufficient data
Solution Approach 1:
The patent combines field data with physics-based simulation data to create a hybrid training dataset. This merging of data sources allows the model to benefit from both real-world measurements and theoretically sound physics-based predictions, improving wear prediction accuracy without relying solely on limited field data
Solution Approach 2:
The patent introduces physics-based simulation as an intermediary data source that bridges the gap between limited field data and comprehensive wear prediction requirements. The simulation generates additional training data that complements field measurements, providing a more robust foundation for model training
2Stability of the object's composition
If conventional models rely solely on field data, then data collection is straightforward, but prediction stability deteriorates due to noise and limitations in field measurements
Solution Approach 1:
The patent converts the limitation of limited field data into an opportunity by incorporating physics-based simulation data. The simulation provides clean, theoretically sound data that compensates for the noise and limitations in field measurements, improving overall prediction stability
Solution Approach 2:
The patent creates a composite training dataset that combines field data and physics-based simulation data. This composite approach leverages the strengths of both data sources while mitigating their individual weaknesses, resulting in more stable and reliable wear predictions
3Measurement precision
If more field data is collected to improve model accuracy, then prediction quality improves, but system complexity and data collection requirements increase
Solution Approach 1:
The patent uses physics-based simulation to create virtual copies of field data that can be generated without physical data collection infrastructure. These simulated data copies provide additional training samples that improve model accuracy without requiring additional sensors, drilling operations, or data collection equipment
Data Source
AI summary
An earth-boring tool system may include a drill string, and at least one or more sensors. The earth-boring tool system may receive data indicative of wear of at least part of a drilling tool, obtain labeled dull grading data for the drilling tool based on the received data, obtain a physics-based bit wear model based on one or more drilling parameters, generate one or more physics-based dull grading data labels based on the physics-based bit wear model, and train an artificial intelligence bit wear model based on the labeled dull grading data and the physics-based dull grading labels.