TY - JOUR
T1 - Classification of reservoir recovery factor for oil and gas reservoirs
T2 - A multi-objective feature selection approach
AU - Al-Tashi, Qasem
AU - Akhir, Emelia Akashah Patah
AU - Abdulkadir, Said Jadid
AU - Mirjalili, Seyedali
AU - Shami, Tareq M.
AU - Alhusssian, Hitham
AU - Alqushaibi, Alawi
AU - Alwadain, Ayed
AU - Balogun, Abdullateef O.
AU - Al-Zidi, Nasser
N1 - Funding Information:
Funding: This research was supported by Universiti Teknologi PETRONAS, under the Yayasan Universiti Teknologi PETRONAS (YUTP) Fundamental Research Grant Scheme (YUTP-FRG/015LC0-274). The Development of Data Quality Metrics to Assess the Quality of Big Datasets. Also, we would like to acknowledge the support by Researchers Supporting Project number (RSP-2021/309), King Saud University, Riyadh, Saudi Arabia.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/8
Y1 - 2021/8
N2 - The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate classification of reservoir recovery factor, as the generated reservoir features are usually heterogeneous. Consequently, it is imperative to select relevant reservoir features while preserving or amplifying reservoir recovery accuracy. This phenomenon can be treated as a multi-objective optimization problem, since there are two conflicting objectives: minimizing the number of measurements and preserving high recovery classification accuracy. In this study, wrapper-based multi-objective feature selection approaches are proposed to estimate the set of Pareto optimal solutions that represents the optimum trade-off between these two objectives. Specifically, three multi-objective optimization algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Particle Swarm Optimization (MOPSO)—are investigated in selecting relevant features from the reservoir dataset. To the best of our knowledge, this is the first time multi-objective optimization has been used for reservoir recovery factor classification. The Artificial Neural Network (ANN) classification algorithm is used to evaluate the selected reservoir features. Findings from the experimental results show that the proposed MOGWO-ANN outperforms the other two approaches (MOPSO and NSGA-II) in terms of producing non-dominated solutions with a small subset of features and reduced classification error rate.
AB - The accurate classification of reservoir recovery factor is dampened by irregularities such as noisy and high-dimensional features associated with the reservoir measurements or characterization. These irregularities, especially a larger number of features, make it difficult to perform accurate classification of reservoir recovery factor, as the generated reservoir features are usually heterogeneous. Consequently, it is imperative to select relevant reservoir features while preserving or amplifying reservoir recovery accuracy. This phenomenon can be treated as a multi-objective optimization problem, since there are two conflicting objectives: minimizing the number of measurements and preserving high recovery classification accuracy. In this study, wrapper-based multi-objective feature selection approaches are proposed to estimate the set of Pareto optimal solutions that represents the optimum trade-off between these two objectives. Specifically, three multi-objective optimization algorithms—Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Particle Swarm Optimization (MOPSO)—are investigated in selecting relevant features from the reservoir dataset. To the best of our knowledge, this is the first time multi-objective optimization has been used for reservoir recovery factor classification. The Artificial Neural Network (ANN) classification algorithm is used to evaluate the selected reservoir features. Findings from the experimental results show that the proposed MOGWO-ANN outperforms the other two approaches (MOPSO and NSGA-II) in terms of producing non-dominated solutions with a small subset of features and reduced classification error rate.
KW - Classification
KW - Feature selection
KW - MOGWO
KW - MOPSO
KW - Multi-objective optimization
KW - NSGA-II
KW - Oil and gas
KW - Recovery factor
KW - Reservoir
UR - http://www.scopus.com/inward/record.url?scp=85113471937&partnerID=8YFLogxK
U2 - 10.3390/jmse9080888
DO - 10.3390/jmse9080888
M3 - Article
AN - SCOPUS:85113471937
VL - 9
JO - Journal of Marine Science and Engineering
JF - Journal of Marine Science and Engineering
SN - 2077-1312
IS - 8
M1 - 888
ER -