TY  - CONF
N2  - 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.
SN  - 9781728154473
TI  - Feature Selection Based on Grey Wolf Optimizer for Oil Gas Reservoir Classification
KW  - Intelligent computing; Nearest neighbor search; Petroleum industry; Petroleum reservoir engineering; Petroleum reservoirs; Proven reserves; Recovery
KW  -  Benchmarking methods; Classification accuracy; High dimensionality; Hydrocarbon reserves; K nearest neighbor (KNN); Optimization algorithms; Selection techniques; Wrapper-based feature selection
KW  -  Feature extraction
Y1  - 2020///
ID  - scholars12620
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097561507&doi=10.1109%2fICCI51257.2020.9247827&partnerID=40&md5=491de640e828ea65f6ece71c79c58ee1
A1  - Al-Tashi, Q.
A1  - Rais, H.M.
A1  - Abdulkadir, S.J.
A1  - Mirjalili, S.
N1  - 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
EP  - 216
PB  - Institute of Electrical and Electronics Engineers Inc.
SP  - 211
AV  - none
ER  -