利用机器学习进行流域指标预测和液压流量单位分类
Tengku Astsauri1, Muhammad Habiburrahman1, Ahmed Farid Ibrahim2,3
1Department of Petroleum Engineering and Geosciences, King Fahd University of Petroleum & Minerals, 31261, Dhahran, Saudi Arabia.
Scientific reports
|February 20, 2024
概括
机器学习准确地预测水库特征的流域指标 (FZI). 这种方法有效地确定了高质量的水库区域,改善了现场开发决策.
科学领域:
- 石油地质科学 石油地质科学
- 机器学习应用 机器学习应用
- 储水库工程 储水库工程
背景情况:
- 储水池的表征对于理解地下异质性至关重要,但由于规模依赖的变化而面临挑战.
- 液压流量单元 (HFU) 划分区域以类似的石质物理和流量特征将岩石组合在一起.
- 流域指标 (FZI) 是HFU确定的一个关键参数,但其测量是昂贵和耗时的.
研究的目的:
- 采用监督和无监督的机器学习算法来预测FZI并将水库分为不同的HFUs.
- 使用各种机器学习技术开发和优化预测模型.
- 通过开发的模型,确定高质量的水库区域.
主要方法:
- 使用无监督K-means集群和监督算法 (随机森林,XGBoost,SVM,ANN) 进行FZI预测和HFU分类.
- 使用来自水库核心分析实验室 (RCAL) 数据的FZI值进行训练和测试的模型.
- 应用了三重交叉验证和随机搜索交叉验证,用于超参数调整和模型优化.
主要成果:
- 监督算法实现了高性能,R平方值为0.89 (训练) 和0.91 (测试).
- 随机森林表现出卓越的性能,R平方值为0.957 (训练) 和0.908 (测试).
- 通过K-means集群和高斯混合模型,成功地将数据分为10个HFU,确定了特定的高潜力水库区域.
结论:
- 机器学习为水库表征的传统方法提供了快速,经济有效和精确的替代方案.
- 开发的模型成功地预测FZI和分类HFU,使高质量的水库潜力能够有效地识别.
- 这种方法通过提供准确的地下洞察力,彻底改变了现场开发中的决策.
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