LSTM Based EFAST Global Sensitivity Analysis for Interwell Connectivity Evaluation Using Injection and Production Fluctuation Data

Haibo Cheng, Valeriy Vyatkin, Evgeny Osipov, Peng Zeng, Haibin Yu*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
26 Downloads (Pure)

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.

Original languageEnglish
Article number9055361
Pages (from-to)67289-67299
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 1 Jan 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Extended Fourier amplitude sensitivity test
  • Global sensitivity analysis
  • Interwell connectivity
  • Long short-term memory
  • Oil and gas field

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