Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification

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...

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Main Authors: Al-Tashi, Q., Rais, H.M., Abdulkadir, S.J., Mirjalili, S.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29882 /
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097561507&doi=10.1109%2fICCI51257.2020.9247827&partnerID=40&md5=491de640e828ea65f6ece71c79c58ee1
http://eprints.utp.edu.my/29882/
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spelling utp-eprints.298822022-03-25T03:05:25Z Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification Al-Tashi, Q. Rais, H.M. Abdulkadir, S.J. Mirjalili, S. 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. Institute of Electrical and Electronics Engineers Inc. 2020 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097561507&doi=10.1109%2fICCI51257.2020.9247827&partnerID=40&md5=491de640e828ea65f6ece71c79c58ee1 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. http://eprints.utp.edu.my/29882/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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.
format Conference or Workshop Item
author Al-Tashi, Q.
Rais, H.M.
Abdulkadir, S.J.
Mirjalili, S.
spellingShingle Al-Tashi, Q.
Rais, H.M.
Abdulkadir, S.J.
Mirjalili, S.
Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification
author_sort Al-Tashi, Q.
title Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification
title_short Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification
title_full Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification
title_fullStr Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification
title_full_unstemmed Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification
title_sort feature selection based on grey wolf optimizer for oil gas reservoir classification
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097561507&doi=10.1109%2fICCI51257.2020.9247827&partnerID=40&md5=491de640e828ea65f6ece71c79c58ee1
http://eprints.utp.edu.my/29882/
_version_ 1741197314242904064
score 11.62408

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