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Optimized Random Vector Functional Link network to predict oil production from Tahe oil field in China

Authors :
Alalimi Ahmed
Pan Lin
Al-qaness Mohammed A.A.
Ewees Ahmed A.
Wang Xiao
Abd Elaziz Mohamed
Source :
Oil & Gas Science and Technology, Vol 76, p 3 (2021)
Publication Year :
2021
Publisher :
EDP Sciences, 2021.

Abstract

In China, Tahe Triassic oil field block 9 reservoir was discovered in 2002 by drilling wells S95 and S100. The distribution of the reservoir sand body is not clear. Therefore, it is necessary to study and to predict oil production from this oil field. In this study, we propose an improved Random Vector Functional Link (RVFL) network to predict oil production from Tahe oil field in China. The Spherical Search Optimizer (SSO) is applied to optimize the RVFL and to enhance its performance, where SSO works as a local search method that improved the parameters of the RVFL. We used a historical dataset of this oil field from 2002 to 2014 collected by a local partner. Our proposed model, called SSO-RVFL, has been evaluated with extensive comparisons to several optimization methods. The outcomes showed that, SSO-RVFL achieved accurate predictions and the SSO outperformed several optimization methods.

Details

Language :
English, French
ISSN :
12944475 and 19538189
Volume :
76
Database :
Directory of Open Access Journals
Journal :
Oil & Gas Science and Technology
Publication Type :
Academic Journal
Accession number :
edsdoj.2f174a85864274a7e4c877a7f40726
Document Type :
article
Full Text :
https://doi.org/10.2516/ogst/2020081