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LSTM Based EFAST Global Sensitivity Analysis for Interwell Connectivity Evaluation Using Injection and Production Fluctuation Data
Cheng HB(程海波)1,2,3,4; Vyatkin, Valeriy5,6; Osipov, Evgeny5; Zeng P(曾鹏)1,2,3; Yu HB(于海斌)1,2,3
Department工业控制网络与系统研究室
Source PublicationIEEE ACCESS
ISSN2169-3536
2020
Volume8Pages:67289-67299
Indexed BySCI ; EI
EI Accession number20201808598528
WOS IDWOS:000527416200005
Contribution Rank1
Funding OrganizationNatural Science Foundation of ChinaNational Natural Science Foundation of China [61533015]
KeywordInterwell connectivity long short-term memory global sensitivity analysis extended Fourier amplitude sensitivity test oil and gas field
Abstract

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.

Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS KeywordNEURAL-NETWORKS ; MODEL ; FIELD
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Funding ProjectNatural Science Foundation of China[61533015]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/26749
Collection工业控制网络与系统研究室
Corresponding AuthorYu HB(于海斌)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
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.University of Chinese Academy of Sciences, Beijing 100049, China
5.Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden
6.Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland
Recommended Citation
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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