一个严格的工作流程和对比分析,以准确地确定使用数据驱动的方法在波斯湾地区的玻璃石反射率
Parsa Kharazi Esfahani1,2, Hadi Mahdavi Basir3, Ahmad Reza Rabbani1
1Department of Petroleum Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424 Hafez Avenue, Box 15875-4413, Tehran, 1591634311, Iran.
Scientific reports
|September 2, 2024
概括
这项研究引入了先进的机器学习模型,ExtraTree和XGBoost,使用Rock-Eval热解数据准确预测玻璃矿石反射率 (VR). 与传统方法相比,这种新的方法显著提高了VR预测的准确性.
科学领域:
- 地质化学 地质化学
- 石油地质科学 石油地质科学
- 机器学习应用 机器学习应用
背景情况:
- 维特里尼特反射率 (VR) 对于评估源岩成熟度至关重要.
- 准确的VR测量是具有挑战性和昂贵的.
- 使用Rock-Eval数据和传统机器学习的现有方法存在局限性.
研究的目的:
- 开发一种精确且具有成本效益的方法来预测玻璃石反射率 (VR).
- 利用先进的机器学习模型与Rock-Eval数据来提高成熟度评估.
主要方法:
- 使用了ExtraTree和XGBoost机器学习模型.
- 采用数据预处理技术:用于异常处理的winorization和用于减小维度的主要组件分析 (PCA).
- 输入变量包括深度,T-max,S1/TOC和来自Rock-Eval热解的HI.
主要成果:
- ExtraTree模型,结合winorization和PCA (Set 1),实现了最高的预测准确度.
- 这种模型的R2得分为0.997,显著超过传统方法.
- 没有数据预处理的模型 (集合3) 显示了最低的准确性,验证了方法的有效性.
结论:
- 先进的机器学习模型,特别是具有优化数据准备的ExtraTree,为VR预测提供了卓越的准确性.
- 这种方法提高了源岩成熟度评估的可靠性和效率.
- 该研究表明,与传统的经验方程和传统的机器学习技术相比,有了显著的进步.
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