eprintid: 12620 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/26/20 datestamp: 2023-11-10 03:27:10 lastmod: 2023-11-10 03:27:10 status_changed: 2023-11-10 01:49:07 type: conference_item metadata_visibility: show creators_name: Al-Tashi, Q. creators_name: Rais, H.M. creators_name: Abdulkadir, S.J. creators_name: Mirjalili, S. title: Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification ispublished: pub keywords: Intelligent computing; Nearest neighbor search; Petroleum industry; Petroleum reservoir engineering; Petroleum reservoirs; Proven reserves; Recovery, Benchmarking methods; Classification accuracy; High dimensionality; Hydrocarbon reserves; K nearest neighbor (KNN); Optimization algorithms; Selection techniques; Wrapper-based feature selection, Feature extraction note: cited By 24; Conference of 2020 International Conference on Computational Intelligence, ICCI 2020 ; Conference Date: 8 October 2020 Through 9 October 2020; Conference Code:164916 abstract: The classification of the hydrocarbon reserve is a significant challenge for both oil and gas producing firms. The factor of reservoir recovery contributes to the proven reservoir growth potential which leads to a good preparation of field development and production. However, the high dimensionality or irrelevant measurements/features of the reservoir data leads to less classification accuracy of the factor reservoir recovery. Therefore, feature selection techniques become a necessity to eliminate the said irrelevant measurements/ features. In this paper, a wrapper-based feature selection method is proposed to select the optimal feature subset. A Binary Grey Wolf Optimization (BGWO) is applied to find the best features/measurements from big reservoir data obtained from U.S.A. oil gas fields. To our knowledge, this is the first time applying the Grey Wolf Optimizer (GWO) as a search technique to search for the most important measurements to achieve high classification accuracy for reservoir recovery factor. The wrapper K-Nearest Neighbors (KNN) classifier is used to evaluate the selected features. In addition, to examine the efficiency of the proposed method, two recent algorithms namely: Whale Optimization algorithm (WAO) and Dragonfly Algorithm (DA) are implemented for comparison. The experimental results showed that, the proposed BGWO-KNN significantly outperforms benchmarking methods in terms of feature reduction as well as increasing the classification accuracy. The proposed method shows a great potential for solving the real oil gas problems. © 2020 IEEE. date: 2020 publisher: Institute of Electrical and Electronics Engineers Inc. official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097561507&doi=10.1109%2fICCI51257.2020.9247827&partnerID=40&md5=491de640e828ea65f6ece71c79c58ee1 id_number: 10.1109/ICCI51257.2020.9247827 full_text_status: none publication: 2020 International Conference on Computational Intelligence, ICCI 2020 pagerange: 211-216 refereed: TRUE isbn: 9781728154473 citation: Al-Tashi, Q. and Rais, H.M. and Abdulkadir, S.J. and Mirjalili, S. (2020) Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification. In: UNSPECIFIED.