{"title":"深度学习算法的步长对压裂处理中压力预测的影响","authors":"Yuxuan Liu, Ziyi Peng, Zhong-Hui Liu, Liansong Wu, Yutong Wu, Jianchun Guo","doi":"10.1080/10916466.2024.2391464","DOIUrl":null,"url":null,"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...","PeriodicalId":19888,"journal":{"name":"Petroleum Science and Technology","volume":"12 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The impact of step sizes on pressure prediction in fracturing treatment via deep learning algorithms\",\"authors\":\"Yuxuan Liu, Ziyi Peng, Zhong-Hui Liu, Liansong Wu, Yutong Wu, Jianchun Guo\",\"doi\":\"10.1080/10916466.2024.2391464\",\"DOIUrl\":null,\"url\":null,\"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...\",\"PeriodicalId\":19888,\"journal\":{\"name\":\"Petroleum Science and Technology\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Petroleum Science and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/10916466.2024.2391464\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Petroleum Science and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10916466.2024.2391464","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
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...
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.
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