页岩水库类型的识别和解释性分析:来自LightGBM和SHAP算法的见解
Luchuan Zhang1,2, Zhiyuan Li1,2, Zhang Lei3
1School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China.
ACS omega
|January 26, 2026
概括
一种新的光梯度增强机 (LightGBM) 模型使用日志记录数据准确识别深层页岩水库类型. 这种机器学习方法提高了预测准确度和效率,而不是传统的水库分级方法.
科学领域:
- 地质科学 地质科学
- 石油工程是石油工程中的一个.
- 机器学习 机器学习
背景情况:
- 传统的方法在深层页岩水库的表征中与复杂的非线性关系作斗争.
- 精确预测页岩水库类型对于有效的资源评估和开发至关重要.
研究的目的:
- 开发和优化用于识别深层页岩水库类型的机器学习模型.
- 使用LightGBM.比较基于分类的与基于回归的方案的性能.
- 评估不同采伐曲线在水库类型识别中的重要性.
主要方法:
- 使用光梯度增强机 (LightGBM) 算法来识别水库类型.
- 实施了SHapley添加式解释 (SHAP) 用于定量特征重要性分析.
- 对比基于分类和基于回归的建模方案.
主要成果:
- 基于分类的LightGBM方案实现了90.5%的加权精度和90.4%的加权回忆,超过了基于回归的方案 (85.9%和86.1%).
- 影响水库类型识别的关键记录曲线包括补偿密度 (DEN),玛射线 (GR),补偿中子 (CNL) 和声学传输时间 (AC).
- SHAP分析揭示了记录回应和水库分类结果之间的复杂非线性关系.
结论:
- 优化的LightGBM模型为深页岩水库类型的识别和分级提供了一种高效准确的方法.
- 机器学习,特别是LightGBM,在复杂的水库表征中比传统方法提供了显著的优势.
- 这种方法为深层页岩水库的综合分级评估提供了一个新的框架.
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