Abstract
AbstractAn 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.
Highlights
In the petroleum industry, the essence of exploration is to convert undiscovered resources to recoverable reserves
The results indicated that about 26% of the study area was delineated as the high petroleum potential class by the classified composite common risk segment (CCRS) map and 73.4% of the testing data were correctly predicted by the proposed model
Overlaying the vector layer of the discovered oil pools on the reclassified map (Figure 10) showed 78.75% of the area of the pools has been located in the class “H.” Comparing the results of the FE and optimized fuzzy ELECTRE (OFE) approaches indicated that the optimization of the weights by the artificial bee colony (ABC) algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration
Summary
The essence of exploration is to convert undiscovered resources to recoverable reserves. Petroleum potential modeling is an important preliminary step in petroleum exploration. There are several approaches for assessing petroleum potential of an area, which can be generally classified as data-driven, knowledge-driven, and hybrid approaches. Data-driven models involve quantitative analysis of spatial relationships between discovered petroleum pools in a region of interest and indirect evidence of petroleum potential. The resulting relationships are used to determine the parameters of the model by which evidence data sets are integrated into a single petroleum potential map. Knowledge-driven methods rely on the judgments of experts who evaluate the relative importance of input data sets and the model parameters [1]
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