Regular Article
Optimized Random Vector Functional Link network to predict oil production from Tahe oil field in China
1
Faculty of Earth Resources, China University of Geoscience, Wuhan 430074, China
2
State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3
Department of Computer, Damietta University, Damietta 34517, Egypt
4
School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk, Russia
* Corresponding author: panlin@cug.edu.cn
Received:
14
June
2020
Accepted:
13
October
2020
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.
© A. Alalimi et al., published by IFP Energies nouvelles, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
