Ahmed Alalimi, Lin Pan, M. A. Al-qaness, A. Ewees, Xiao Wang, M. A. Abd Elaziz
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Optimized Random Vector Functional Link network to predict oil production from Tahe oil field in China
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.
期刊介绍:
OGST - Revue d''IFP Energies nouvelles is a journal concerning all disciplines and fields relevant to exploration, production, refining, petrochemicals, and the use and economics of petroleum, natural gas, and other sources of energy, in particular alternative energies with in view of the energy transition.
OGST - Revue d''IFP Energies nouvelles has an Editorial Committee made up of 15 leading European personalities from universities and from industry, and is indexed in the major international bibliographical databases.
The journal publishes review articles, in English or in French, and topical issues, giving an overview of the contributions of complementary disciplines in tackling contemporary problems. Each article includes a detailed abstract in English. However, a French translation of the summaries can be provided to readers on request. Summaries of all papers published in the revue from 1974 can be consulted on this site. Over 1 000 papers that have been published since 1997 are freely available in full text form (as pdf files). Currently, over 10 000 downloads are recorded per month.
Researchers in the above fields are invited to submit an article. Rigorous selection of the articles is ensured by a review process that involves IFPEN and external experts as well as the members of the editorial committee. It is preferable to submit the articles in English, either as independent papers or in association with one of the upcoming topical issues.