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.

中文翻译：

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用于在石油系统水平评估石油潜力的 ABC 优化模糊 ELECTRE 方法

摘要 现代石油勘探不可避免的一个方面是同时考虑大型、复杂和不同的空间数据集。在此背景下，本文提出了基于结合人工蜂群 (ABC) 优化算法、模糊逻辑和排名方法的优化模糊电 (OFE) 方法，以在空间框架中评估石油系统级别的石油潜力。专家的知识和已发现的石油储集层中可用的信息。它使用石油系统基本要素的特性作为关键标准。为了演示该方法，我们对威利斯顿盆地的红河石油系统进行了案例研究。完成各种预处理步骤后，使用 OFE 整合了与标准相关的八个空间数据集，以生成一张地图，从而可以划定具有最高石油潜力和最低风险的区域，以便进行进一步的探索性调查。成功率和预测率曲线用于衡量模型的性能。成功率和预测准确率都在 80-90% 的范围内，表明模型性能优异。考虑到五级石油潜力，所提出的方法优于先前研究中使用的空间模型。此外，比较FE和OFE的结果表明，ABC算法对权重的优化提高了约15%的准确度，即石油勘探的成功率相对较高，风险较低。