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在高度异质的紧密碳酸盐储中进行透性建模,使用基于学习和基于合适的方法进行比较评估

Ehsan Hajibolouri¹, Ali Akbar Roozshenas², Rohaldin Miri^3,4

¹Petroleum Engineering Program, School of Mining & Geosciences, Nazarbayev University, 010000, Astana, Kazakhstan.

Scientific reports

|May 3, 2024

概括

机器学习模型通过整合井日志数据,显著改善了复杂碳酸盐水库的透性预测. 这种方法克服了传统方法的局限性,增强了用于模拟的水库表征.

科学领域:

石油地质科学石油地质科学
储水库工程储水库工程
机器学习应用机器学习应用

背景情况:

透度建模对于水库模拟至关重要,但在异质碳酸盐构成中具有挑战性.
传统的静态岩石类型和基于合的模型通常会因为碳酸盐中的孔隙-透性数据散射而失败.
石化物理井日志数据提供连续和全面的信息,以改善透性预测.

研究的目的:

开发和比较机器学习 (ML) 和适配模型,用于预测紧密碳酸盐储的无芯间隔的透性.
通过使用井日志数据,评估ML模型与传统指数和统计拟合方法的有效性.
提高透性分布的准确性,以改进3D水库建模和模拟.

主要方法:

利用透性,透性和石化物理井日志数据从两个油井在一个紧密的碳酸盐水库.
开发了使用机器学习算法 (随机森林) 和拟合技术的预测性透性模型.
使用根平均平方误差 (RMSE) 将ML模型的性能与指数和统计拟合模型进行了比较.

主要成果:

集成的ML透性模型,特别是Random Forest,显示出比基于装配的方法更高的性能.
实现了水平和垂直透气性预测的低RMSE值:3.7和4.5 (井2),以及1.7和0.86 (井4).
通过机器学习将井日志数据纳入,通过整合水库岩石物理来改善透性建模.

结论:

机器学习,特别是随机森林,提供了一个强大的解决方案,用于在复杂,异构的碳酸盐储库中准确预测透性.
与传统技术相比,开发的ML模型为透率估计提供了更可靠的方法.
这项研究增强了水库的表征,从而改善了3D透性模型,以实现更准确的动态水库模拟.

关键词:

数据建模数据建模异质性异质性异质性机器学习是机器学习.透度建模的模型.随机的森林随机的森林储水池模拟器储水池模拟器

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