Optimization of Well Control during Gas Flooding Using the Deep-LSTM-Based Proxy Model: A Case Study in the Baoshaceng Reservoir, Tarim, China

Qihong Feng, Kuankuan Wu, Jiyuan Zhang, Sen Wang, Xianmin Zhang, Daiyu Zhou and An Zhao
Additional contact information
Qihong Feng: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Kuankuan Wu: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Jiyuan Zhang: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Sen Wang: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Xianmin Zhang: School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
Daiyu Zhou: Tarim Oilfield Company, China National Petroleum Corporation, Korla 841000, China
An Zhao: Tarim Oilfield Company, China National Petroleum Corporation, Korla 841000, China

Energies, 2022, vol. 15, issue 7, 1-14

Abstract: Gas flooding has proven to be a promising method of enhanced oil recovery (EOR) for mature water-flooding reservoirs. The determination of optimal well control parameters is an essential step for proper and economic development of underground hydrocarbon resources using gas injection. Generally, the optimization of well control parameters in gas flooding requires the use of compositional numerical simulation for forecasting the production dynamics, which is computationally expensive and time-consuming. This paper proposes the use of a deep long-short-term memory neural network (Deep-LSTM) as a proxy model for a compositional numerical simulator in order to accelerate the optimization speed. The Deep-LSTM model was integrated with the classical covariance matrix adaptive evolutionary (CMA-ES) algorithm to conduct well injection and production optimization in gas flooding. The proposed method was applied in the Baoshaceng reservoir of the Tarim oilfield, and shows comparable accuracy (with an error of less than 3%) but significantly improved efficiency (reduced computational duration of ~90%) against the conventional numerical simulation method.

Keywords: gas flooding; well control optimization; deep long short-term memory neural network; proxy model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/7/2398/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/7/2398/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:7:p:2398-:d:779046

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().