The most crucial elements in the oil and gas sector are predicting subsurface lithofacies utilizing geophysical logs for reservoir characterization and sweet spot assessment procedures. Nevertheless, accurately predicting payable lithofacies in a complex heterogeneous geological setting, such as the lower goru formation, poses considerable difficulty because conventional methods fall short in delivering highly accurate outcomes. Hence, this research proposes an advanced cost and time-saving data intelligence strategy using multiple classifiers to predict lithofacies with maximum accuracy that will aid in sweet spot evaluation in oil and gas fields globally. Geophysical log data of five wells from a mature gas field were used. The targeted reservoir formation was classified into seven facies types. We evaluated the performance of seven different models: support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DTr), naive Bayes (NB), adaptive boosting (AB), and ensemble (an integrated SVM, KNN, RF, and DTr classifier). RF and ensemble classifiers predicted the lithofacies with accuracies of 97.5 and 97.3%, respectively. Their efficacy in lithofacies prediction with high accuracy renders them as valuable tools in the domain of sweet spot evaluation. The proposed digital intelligence strategy could help operators identify drilling sites based on in-depth reservoir characterizations.

石油和天然气领域最关键的要素是利用地球物理测井来预测地下岩相，以进行储层表征和最佳点评估程序。然而，在复杂的异质地质环境（例如下戈鲁地层）中准确预测可开采岩相存在相当大的困难，因为传统方法无法提供高度准确的结果。因此，这项研究提出了一种先进的节省成本和时间的数据智能策略，使用多个分类器来以最大精度预测岩相，这将有助于全球油气田的最佳点评估。使用了成熟气田五口井的地球物理测井数据。将目标储层划分为七种相类型。我们评估了七种不同模型的性能：支持向量机 (SVM)、K 最近邻 (KNN)、随机森林 (RF)、决策树 (DTr)、朴素贝叶斯 (NB)、自适应提升 (AB) 和集成（集成的 SVM、KNN、RF 和 DTr 分类器）。 RF分类器和集合分类器预测岩相的准确率分别为97.5%和97.3%。它们在岩相预测中的高精度功效使它们成为最佳点评估领域的有价值的工具。所提出的数字智能策略可以帮助操作员根据深入的油藏特征来识别钻井地点。