深度学习算法的步长对压裂处理中压力预测的影响

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

摘要

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...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

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

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...

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

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.