Abstract
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.
Highlights
The petroleum sector is characterized by a variety of uncertainties as requirements for making critical investment decisions
The findings demonstrated that Multi-Objective Grey Wolf Optimizer (MOGWO)-Artificial Neural Network (ANN) outperforms the other two algorithms in most cases
Amulti-objective feature selection approach based on three algorithms, namely, MOGWO, Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), was proposed for the classification of reservoir recovery factor
Summary
The petroleum sector is characterized by a variety of uncertainties as requirements for making critical investment decisions To reduce these uncertainties, many approaches have recently been implemented in critical sectors like data management, reserve assessment, and reservoir characterization [1]. Most exploration and production firms consider the recovery factor to be a crucial metric, during the reservoir’s initial life. This is based on the fact that most investment choices are predicated on the quantity of hydrocarbon that can be recovered from the target inventory using present methods and operating practices [2]. Understanding the reservoir range as well as the recovery rate will aid in effective hydrocarbon production planning
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