LSTM Based EFAST Global Sensitivity Analysis for Interwell Connectivity Evaluation Using Injection and Production Fluctuation Data | |
Cheng HB(程海波)2,3,4,5; Vyatkin, Valeriy1,6; Osipov, Evgeny6; Zeng P(曾鹏)2,3,4; Yu HB(于海斌)2,3,4 | |
刊名 | IEEE ACCESS |
2020 | |
卷号 | 8页码:67289-67299 |
关键词 | Interwell connectivity long short-term memory global sensitivity analysis extended Fourier amplitude sensitivity test oil and gas field |
ISSN号 | 2169-3536 |
产权排序 | 1 |
英文摘要 | In petroleum production system, interwell connectivity evaluation is a significant process to understand reservoir properties comprehensively, determine water injection rate scientifically, and enhance oil recovery effectively for oil and gas field. In this paper, a novel long short-term memory (LSTM) neural network based global sensitivity analysis (GSA) method is proposed to analyse injector-producer relationship. LSTM neural network is employed to build up the mapping relationship between production wells and surrounding injection wells using the massive historical injection and production fluctuation data of a synthetic reservoir model. Next, the extended Fourier amplitude sensitivity test (EFAST) based GSA approach is utilized to evaluate interwell connectivity on the basis of the generated LSTM model. Finally, the presented LSTM based EFAST sensitivity analysis method is applied to a benchmark test and a synthetic reservoir model. Experimental results show that the proposed technique is an efficient method for estimating interwell connectivity. |
资助项目 | Natural Science Foundation of China[61533015] |
WOS关键词 | NEURAL-NETWORKS ; MODEL ; FIELD |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
语种 | 英语 |
WOS记录号 | WOS:000527416200005 |
资助机构 | Natural Science Foundation of ChinaNational Natural Science Foundation of China [61533015] |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/26749] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Yu HB(于海斌) |
作者单位 | 1.Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 4.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 5.University of Chinese Academy of Sciences, Beijing 100049, China 6.Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden |
推荐引用方式 GB/T 7714 | Cheng HB,Vyatkin, Valeriy,Osipov, Evgeny,et al. LSTM Based EFAST Global Sensitivity Analysis for Interwell Connectivity Evaluation Using Injection and Production Fluctuation Data[J]. IEEE ACCESS,2020,8:67289-67299. |
APA | Cheng HB,Vyatkin, Valeriy,Osipov, Evgeny,Zeng P,&Yu HB.(2020).LSTM Based EFAST Global Sensitivity Analysis for Interwell Connectivity Evaluation Using Injection and Production Fluctuation Data.IEEE ACCESS,8,67289-67299. |
MLA | Cheng HB,et al."LSTM Based EFAST Global Sensitivity Analysis for Interwell Connectivity Evaluation Using Injection and Production Fluctuation Data".IEEE ACCESS 8(2020):67289-67299. |
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