Tree-Based Ensemble Algorithms for Lithofacies Classification and Permeability Prediction in Heterogeneous Carbonate Reservoirs

Abstract

Abstract Rock facies are typically identified either by core analysis to provide visually interpreted lithofacies, or determined indirectly based on suites of recorded well-log data, thereby generating electrofacies interpretations. Since the lithofacies cannot be obtained for all reservoir intervals, drilled section and/or wells, it is commonly essential to model the discrete lithofacies as a function of well-log data (electrofacies) to predict the poorly sampled or non-cored intervals. The process is called predictive lithofacies classification. In this study, measured discrete lithofacies distributions (based on core data) are comparatively modeled with well-log data using two tree-based ensemble algorithms: extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost) configured as classifiers. The predicted lithofacies are then combined with recorded well-log data for analysis by an XGBoost regression model to predict permeability. The input well-log variables are log porosity, gamma ray, water saturation, neutron porosity, deep resistivity, and bulk density. The data are derived from the Mishrif carbonate reservoir in a giant southern Iraqi oil field. For efficient lithofacies classification and permeability modelling, random sub-sampling cross-validation was applied to the well-log dataset to generate two subsets: training subset for model tuning; and testing subset for prediction of data points unseen during training of the model. Confusion matrices and the total correct percentage (TCP) of predictions are used to measure the prediction performance of each algorithm to identify the most realistic lithofacies classification. The TCPs for XGBoost and AdaBoost classifiers for the training subset were 98% and 100%, respectively. However, the TCPs achieved for the testing subsets were 97%, and 96%, respectively. The mismatch between the measured and predicted permeability from the XGBoost regressor was determined using root mean square error. The XGBoost model provides accurate lithofacies classification and permeability predictions of the cored data. The XGBoost model is therefore considered suitable for providing reliable predictions of lithofacies and permeability for the non-cored intervals of the same well and for non-cored wells in the studied reservoir. The workflow for lithofacies and permeability prediction was fully implemented and visualized using R open-source codes.

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