Machine learning with hyperparameter optimization applied in facies-supported permeability modeling in carbonate oil reservoirs

Most carbonate reservoirs exhibit heterogeneous pore distribution, whereby the matrix displays low permeability, thus impeding the flow of oil. On the other hand, highly permeable fractures function as the main flow conduits within such reservoirs. Permeability measurements are obtained from core and well test analysis, which are too expensive and not available for many wells. Therefore, accurate permeability prediction is a vital step in developing an efficient field development plan, as it plays a pivotal role in the accurate distribution of 3D petrophysical properties throughout a reservoir. Machine learning (ML) algorithms are now widely applied to predict core permeability using conventional well logs to build a model for permeability prediction in uncored wells. This review considers the performance of six ML algorithms (LightGBM, CATBoost, XGBoost, Adaboost, random forest and gradient boosting) for permeability prediction from a high-quality dataset. The dataset incorporates multiple well-log inputs (gamma ray, caliper, density, neutron porosity, shallow and deep resistivity, total porosity, spontaneous potential, water saturation, depth, and facies) in addition to direct core permeability and porosity measurements. Data pre-processing techniques applied include missing data imputation, scale correction, normalization with three different transformations (log, Box-Cox, and NST) and outlier detection. To enhance the ML performance, two search algorithms (random search and Bayesian optimization) are compared in their ability to tune the ML hyperparameters. There is a need to identify a suitable parameter space, especially when the target variable range is changing. ML performance was evaluated with four evaluation metrics (RMSE, MAE, R², and Adjusted R²). Results showed that the XGBoost algorithm with configuration of (RS as search algorithm, Box Cox as the normalization method, Z-score for outlier detection, without scale correction, old parameter space) delivered the best prediction performance for permeability with RMSE values of 6.9 md and 9.78 md for training and testing, respectively.