The impact of step sizes on pressure prediction in fracturing treatment via deep learning algorithms

IF 1.3 4区工程技术 Q4 ENERGY & FUELS

Petroleum Science and Technology Pub Date : 2024-08-19 DOI:10.1080/10916466.2024.2391464

Yuxuan Liu, Ziyi Peng, Zhong-Hui Liu, Liansong Wu, Yutong Wu, Jianchun Guo

引用次数: 0

Abstract

Hydraulic fracturing relies on accurate pressure prediction for effective risk management and treatment evaluation. Deep learning models such as artificial neural networks, convolutional neural net...

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深度学习算法的步长对压裂处理中压力预测的影响

水力压裂依靠精确的压力预测来进行有效的风险管理和处理评估。深度学习模型，如人工神经网络、卷积神经网络...

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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.