{"title":"基于人工神经网络技术和生产性能数据检测方法的裂缝分布预测和有效性研究：塔里木盆地塔河油田奥陶系碳酸盐岩地层案例研究","authors":"Hailong Ma, Lin Jiang, Zhen Wang, Huan Wen, Changjian Zhang, Pengyuan Han, Ziyou Zhang","doi":"10.1080/10916466.2024.2328786","DOIUrl":null,"url":null,"abstract":"To solve the difficulty of predicting the spatial distribution of fracture linear density and clarify the controlling influence of tectonic factors on the effectiveness of fractures. This article c...","PeriodicalId":19888,"journal":{"name":"Petroleum Science and Technology","volume":"48 1","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction and effectiveness study of fracture distribution based on an artificial neural network technology and production performance data detection method: a case study of Ordovician carbonate formation in the Tahe oilfield, Tarim Basin\",\"authors\":\"Hailong Ma, Lin Jiang, Zhen Wang, Huan Wen, Changjian Zhang, Pengyuan Han, Ziyou Zhang\",\"doi\":\"10.1080/10916466.2024.2328786\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To solve the difficulty of predicting the spatial distribution of fracture linear density and clarify the controlling influence of tectonic factors on the effectiveness of fractures. This article c...\",\"PeriodicalId\":19888,\"journal\":{\"name\":\"Petroleum Science and Technology\",\"volume\":\"48 1\",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2024-03-18\",\"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/10.1080/10916466.2024.2328786\",\"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/10.1080/10916466.2024.2328786","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}
Prediction and effectiveness study of fracture distribution based on an artificial neural network technology and production performance data detection method: a case study of Ordovician carbonate formation in the Tahe oilfield, Tarim Basin
To solve the difficulty of predicting the spatial distribution of fracture linear density and clarify the controlling influence of tectonic factors on the effectiveness of fractures. This article c...
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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