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机器学习辅助预测孔隙性和相关属性使用数字岩石图像

Md Irfan Khan¹, Aaditya Khanal^1,2,3

¹The Jasper Department of Chemical Engineering, The University of Texas at Tyler, Tyler, Texas 75799, United States.

|July 22, 2024

概括

机器学习从微型CT图像中预测岩石特性,改善流体流动建模. 这种高效的工作流可以准确地估计毛孔度,毛孔表面积和喉面积,从而提高了水库的特征.

科学领域:

地质科学地质科学
计算科学计算科学
材料科学材料科学材料科学

背景情况:

估计水库岩石特性对于流体流动和储存建模至关重要.
测量岩石特性的传统实验室方法耗时且昂贵.
微计算机断层扫描 (micro-CT) 可以提供多孔介质的详细3D图像.

研究的目的:

开发一种高效的机器学习工作流程,用于从微型CT图像中预测岩石特性.
提高水库表征的准确性和降低计算成本.
建立一个数据驱动的方法,使用孔网参数来预测绝对透度.

主要方法:

利用卷积神经网络 (CNN) 框架进行图像分析和属性预测.
在各种砂岩样本上实施数据预处理,标签提取和模型培训/验证.
采用机器学习算法,包括极端梯度提升 (XGBoost),用于回归建模.

主要成果:

通过使用精致的CNN模型,实现了孔隙 (2.19%MAPE),毛孔表面面积 (3.04%MAPE) 和喉面积 (6.08%MAPE) 的高预测精度.
开发了一个XGBoost模型,预测了未见的砂岩数据的R2为0.813的绝对透率.
确定扭曲度是影响绝对透率预测的关键因素.

结论:

机器学习模型可以从数字岩石图像中可靠地预测多孔介质的形态和石质特性.
这种计算效率高的方法比传统的水库表征方法有了显著的进步.
开发的工作流程有助于在多孔介质中更准确的流体流量建模.

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