An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

Abstract

An inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

A novel Mahalanobis distance method for predicting oil and gas resource spatial distribution

“…The hybrid method combines the merits of the knowledge- and data-driven methods to predict the spatial oil and gas distribution in the target reservoirs. For example: optimized fuzzy ELECTRE method (Hamzeh and Karimipour, 2020a), optimized fuzzy preference ranking organization method (Hamzeh and Karimipour, 2020a).…”