Abstract
Identifying reservoir fractures has been a challenge due to its significant influence in drilling and production, especially in highly complex carbonate reservoirs. In this paper, one of the well-known carbonate reservoirs, Asmari reservoir located in the Middle East, was considered and its fracture zones were classified as a machine learning nonlinear problem. First, a dataset was created which included conventional logs and fracture points from oil-based micro imager (OBMI) and oil mud reservoir imager (OMRI) with two circumferential and ultrasonic borehole imagers data from four wells. Next, the initial data were considered and the outliers were eliminated. The feature selection was carried out among conventional logs through pattern recognition artificial neural network (ANN) with the second version of the non-dominated sorting Genetic algorithm (NSGA-II). As a result of ANN-NSGA-II, six factors were selected and fed for classification methods. The targets of methods were three classes including no fracture, low fracture, and high fracture zones based on the user-defined thresholds. According to classification accuracies and confusion matrices, the support vector machine (SVM) with cubic kernel function outperformed the fine decision trees, quadratic discriminant analysis, and K-nearest neighbor (KNN) classifier methods. After determining the highest rank by SVM, five other kernel functions were tried as SVM functions. The radial basis function (RBF) with SVM showed a more reliable classification in comparison with the others. Regarding the objective of the present study, means introduced the most effective fracture classification. SVM was run with three neutral-inspired optimization producers including particle swarm optimization (PSO) and two newly presented grasshopper optimization algorithm (GOA) and grey wolf optimizer (GWO) methods. The final results indicated that (1) the hybrid SVM (RBF)-GWO offered superior accuracies in different population of algorithms, (2) classification zones with no fracture and low fracture (classes 1 and 2) had minimum misclassification through SVM-GWO while the high fracture points (class 3) revealed the highest accuracies with SVM-PSO. Also, SVM-GOA offered the best solution in several iterations and higher time, while, SVM-GWO and SVM-PSO seek SVM classification response faster and are thus recommended for fracture detection