Multiple machine learning algorithms for lithofacies prediction in the deltaic depositional system of the lower Goru Formation, Lower Indus Basin, Pakistan.

ABSTRACT: Machine learning techniques for lithology prediction using wireline logs have gained prominence in petroleum reservoir characterization due to the cost and time constraints of traditional methods such as core sampling and manual log interpretation. This study evaluates and compares several machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Logistic Regression (LR), for their effectiveness in predicting lithofacies using wireline logs within the Basal Sand of the Lower Goru Formation, Lower Indus Basin, Pakistan. The Basal Sand of Lower Goru Formation contains four typical lithologies: sandstone, shaly sandstone, sandy shale and shale. Wireline logs from six wells were an

SUBMITTER: Saleem MA 

PROVIDER: S-EPMC12504417 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature