由岩石物理及其解释指导的井日志的基于机器学习的预测
Ji Zhang1, Guiping Liu1, Zhen Wei1
1School of Geology and Mining Engineering, Xinjiang University, Urumqi 830046, China.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
机器学习 (ML) 模型用于井日志分析,以岩石物理为指导,通过夏普利添加式扩展 (SHAP) 显示一致的模式. 即使缺少数据,ML和SHAP也与岩石物理原理保持一致,消除了黑盒子的神秘性.
科学领域:
- 地质物理学 地质物理学
- 机器学习 机器学习
- 石油工程是石油工程中的一个.
背景情况:
- 传统的井日志提炼使用岩石物理模型具有局限性.
- 机器学习 (ML) 提供了一个替代方案,但往往缺乏明确的解释性.
- 将ML与岩石物理原理相结合,是井日志分析的新方法.
研究的目的:
- 用四个ML算法预测毛孔度和粘土体积分数.
- 通过SHAP分析评估ML预测与岩石物理原理的一致性.
- 在井日志分析中探索ML模型的可解释性.
主要方法:
- 采用随机森林 (RF),梯度增强决策树 (GBDT),多层感知子 (MLP) 和线性回归 (LR).
- 使用岩石物理原理指导特征工程和解释 (加德纳和拉里诺诺夫关系).
- 利用夏普利增量解释 (SHAP) 来分析算法行为和解释预测.
主要成果:
- 透度和粘土体积分数的令人满意的预测得到了实现.
- SHAP分析显示了所有四个ML算法的一致模式,与岩石物理原理保持一致.
- 机器学习算法和SHAP分析证明了对岩石物理因果关系的坚持,即使关键输入特征被遗漏.
结论:
- 机器学习与岩石物理原理的整合提高了井日志分析的可解释性.
- SHAP分析提供了数学预测和ML模型的哲学理解之间的桥梁.
- 这种方法超越了ML的传统"黑子"性质,为地质科学数据提供了更深入的见解.
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