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LSTM Based EFAST Global Sensitivity Analysis for Interwell Connectivity Evaluation Using Injection and Production Fluctuation Data
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China. University of Chinese Academy of Sciences, Beijing 100049, China.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland.ORCID iD: 0000-0002-9315-9920
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0069-640x
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
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2020 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 67289-67299Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2020. Vol. 8, p. 67289-67299
Keywords [en]
Interwell connectivity, long short-term memory, global sensitivity analysis, extended Fourier amplitude sensitivity test, oil and gas field
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-78806DOI: 10.1109/ACCESS.2020.2985230ISI: 000527416200005Scopus ID: 2-s2.0-85083982138OAI: oai:DiVA.org:ltu-78806DiVA, id: diva2:1428914
Note

Validerad;2020;Nivå 2;2020-05-07 (alebob)

Available from: 2020-05-07 Created: 2020-05-07 Last updated: 2020-05-07Bibliographically approved

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Vyatkin, ValeriyOsipov, Evgeny

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