Classification of Reservoir Recovery Factor for Oil and Gas Reservoirs: A Multi-Objective Feature Selection Approach

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.

Population Initialization Factor in Binary Multi-Objective Grey Wolf Optimization for Features Selection

“…ANN classifiers were used in this study that will first be introduced with GWO. He also implements the proposed method in oil and gas datasets [7]. As a result, it obtains good results compared with MOPSO and NSGA-II methods.…”

“…It is less efficient, easily traps local optima, and has scalability issues when dealing with high-dimensional datasets. Many studies, such as [7][8] [9], demonstrated that metaheuristic outperform the traditional method. The embedded model has overcome the limitation of the filter and wrapper model by executing it before creating the classifier.…”

An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-II

“…The standard NSGA algorithm has faced criticism for its high computational cost in non-dominant sorting, Absence of elitism, and Necessity for Specifying the Sharing Parameter, that explains why we're adopting NSGA-II to overcome above aforementioned limitations. The most well-known multiobjective optimization algorithm, NSGA-II, is characterised by three key characteristics: a quick nondominated sorting technique, a quick estimation of the crowded distance parameter, and a quick comparison operator [16]. According to Babajamali et al [17], the NSGA-II optimization approach performed much better than PAES and SPEA in specifying a larger variety of approaches to several test issues from previous research.…”

Single candidate optimizer: a novel optimization algorithm

“…In (6), the updated dimension of a candidate solution is assigned the same value as the global best value if the updated position goes out of boundaries.…”

“…To overcome the limitations of the conventional approaches, meta-heuristic algorithms, as a stochastic approach, can be used to solve complicated real-world optimization problems. Meta-heuristic algorithms have shown robust performance when applied to different optimization problems in various fields such as wireless communications [3][4][5] and artificial intelligence [6][7][8].…”