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Knowledge-Based Machine learning for Real-Time rock strength testing while Drilling: Bridging Simulation and Reality
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AI
DOI：10.1016/j.measurement.2025.116664.png)
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Abstract
En 中文
This paper proposes a method for predicting rock strength based on the fusion of physical information from while-drilling tests. Using Boussinesq's elastic half-space theory, a fundamental mechanical model for uniaxial compressive strength based on drilling parameters is developed. Through model experiments, we derive empirical formulas for the uniaxial compressive strength of carbonaceous slate, granite, and sandstone. Meanwhile, we construct a sample library for different lithological types using field test data.The findings indicate that the mechanical model performs poorly in anisotropic formations such as carbonaceous slate. While fully datadriven AI methods show high dependency on the quantity of labeled data, supervised learning models (KNN, RF, GBDT, ANN, 1D-CNN) can achieve high accuracy given sufficient labeled data. However, unsupervised learning techniques like K-means clustering exhibit limited effectiveness. The fusion of physical principles with machine learning techniques addresses these challenges effectively, achieving high prediction accuracy even in the absence of labeled data, with the best model achieving an R2 of 0.82. Additionally, SHAP interpretability methods are employed to explore the influence of drilling parameters on the model's decision-making and their interactions. This framework combines the interpretability of physical models with the adaptability of AI, ensuring effective generalization from simulated experiments to real geological environments.
Keywords:
Intelligent Real-time
In-situ Rock Strength Testing
Physics-Informed Machine Learning
Explainable AI Methods
Multi-Scenario Adaptability
Journal
IF:
5.6
Papers:
2.0W
Citations:
5.4W
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