使用Xgboost方法估计混合页岩的总有机碳含量,以及对页岩油勘探的影响
Yuhang Zhang1,2, Guanlong Zhang3, Weiwei Zhao4,5
1School of Earth Sciences and Engineering, Xi'an Shiyou University, Xi'an, 710065, China. yhzhang@xsyu.edu.cn.
Scientific reports
|September 6, 2024
概括
本研究使用机器学习对页岩中总有机碳 (TOC) 估计,通过减少数据尺寸来提高准确性. 改进的模型确定了有前途的页岩油勘探目标.
科学领域:
- 地质化学 地质化学
- 石油地质学 石油地质学
- 机器学习应用 机器学习应用
背景情况:
- 准确估计总有机碳 (TOC) 对于评估页岩油潜力至关重要.
- 混合碳酸盐和类页岩构造对TOC预测提出了独特的挑战.
- 格尔盆地的哈桑地区是页岩油勘探的关键地区.
研究的目的:
- 用Xgboost方法估计混合碳酸盐和类页岩中的TOC.
- 通过应用主要组件分析 (PCA) 来提高TOC估计的准确性,以减少维度.
- 根据改进的TOC分布映射,确定潜在的页岩油勘探目标.
主要方法:
- 机器学习算法XGBoost (极端梯度提升) 用于初始TOC估计.
- 主要组件分析 (PCA) 用于将井日志数据的维度从五个维度减少到两个维度.
- 对TOC分布的异构图绘制,以划定勘探目标.
主要成果:
- 最初的XGBoost模型显示了适度的TOC估计性能 (R2=0.54),归因于一个小数据集.
- 通过减少PCA的维度,预测和测量TOC之间的相关系数显著提高到0.68.
- 重新绘制的TOC分布地图显示,在城形成区内有两个不同的页岩油勘探目标.
结论:
- 结合的XGBoost和PCA方法为研究区域的TOC估计提供了可靠的方法.
- 确定的勘探目标为页岩油勘探和开发提供了宝贵的见解.
- 这种方法作为类似地质环境和碳化合物勘探研究的强有力的例子.
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