Clastic facies classification using machine learning-based algorithms: A case study from Rawat Basin, Sudan

Machine learning techniques and a dataset of five wells from the Rawat oilfield in Sudan containing 93,925 samples per feature (seven well logs and one facies log) were used to classify four facies. Data pre-processing and preparation involve two processes: data cleaning and feature scaling. Several machine learning algorithms, including Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) for classification, were tested using different iterations and various combinations of features and parameters. The support vector radial kernel training model achieved an accuracy of 72.49% without grid search and 64.02% with grid search, while the blind-well test scores were 71.01% and 69.67%, respectively. The Decision Tree (DT) Hyperparameter Optimization model showed an accuracy of 64.15% for training and 67.45% for testing. In comparison, the Decision Tree coupled with grid search yielded better results, with a training score of 69.91% and a testing score of 67.89%. The model's validation was carried out using the blind well validation approach, which achieved an accuracy of 69.81%. Three algorithms were used to generate the gradient-boosting model. During training, the Gradient Boosting classifier achieved an accuracy score of 71.57%, and during testing, it achieved 69.89%. The Grid Search model achieved a higher accuracy score of 72.14% during testing. The Extreme Gradient Boosting model had the lowest accuracy score, with only 66.13% for training and 66.12% for testing. For validation, the Gradient Boosting (GB) classifier model achieved an accuracy score of 75.41% on the blind well test, while the Gradient Boosting with Grid Search achieved an accuracy score of 71.36%. The Enhanced Random Forest and Random Forest with Bagging algorithms were the most effective, with validation accuracies of 78.30% and 79.18%, respectively. However, the Random Forest and Random Forest with Grid Search models displayed significant variance between their training and testing scores, indicating the potential for overfitting. Random Forest (RF) and Gradient Boosting (GB) are highly effective for facies classification because they handle complex relationships and provide high predictive accuracy. The choice between the two depends on specific project requirements, including interpretability, computational resources, and data nature.