Classification of reservoir recovery factor for oil and gas reservoirs: A multi-objective feature selection approach

Qasem Al-Tashi, Emelia Akashah Patah Akhir, Said Jadid Abdulkadir, Seyedali Mirjalili, Tareq M. Shami, Hitham Alhusssian, Alawi Alqushaibi, Ayed Alwadain, Abdullateef O. Balogun, Nasser Al-Zidi

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Article number888
JournalJournal of Marine Science and Engineering
Issue number8
Publication statusPublished - Aug 2021


  • Classification
  • Feature selection
  • Multi-objective optimization
  • Oil and gas
  • Recovery factor
  • Reservoir


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