Research progress of machine-learning algorithm for formation pore pressure prediction

IF 1.3 4区工程技术 Q4 ENERGY & FUELS

Petroleum Science and Technology Pub Date : 2024-01-03 DOI:10.1080/10916466.2023.2299711

Haoyu Pan, Song Deng, Chaowei Li, Yanshuai Sun, Yanhong Zhao, Lin Shi, Chao Hu

引用次数: 0

Abstract

Formation pore pressure is one of the most important basic data in petroleum exploration and development. The traditional prediction model of formation pore pressure relies on artificial experience...

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地层孔隙压力预测的机器学习算法研究进展

地层孔隙压力是石油勘探和开发中最重要的基础数据之一。传统的地层孔隙压力预测模型依赖于人工经验...

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来源期刊

Petroleum Science and Technology 工程技术-工程：化工

CiteScore

2.90

自引率

13.30%

发文量

277

审稿时长

2.7 months

期刊介绍： The international journal of Petroleum Science and Technology publishes original, high-quality peer-reviewed research and review articles that explore: -The fundamental science of fluid-fluid and rock-fluids interactions and multi-phase interfacial and transport phenomena through porous media related to advanced petroleum recovery processes, -The application of novel concepts and processes for enhancing recovery of subsurface energy resources in a carbon-sensitive manner, -Case studies of scaling up the laboratory research findings to field pilots and field-wide applications. -Other salient technological challenges facing the petroleum industry.