Hyperparameter Optimization of Tree-Based Machine Learning (TB-ML) to Predict Permeability of a Heterogeneous Carbonate Oil Reservoir

Abstract

Permeability is a crucial petrophysical attribute to be accurately estimated due to its direct influence on reservoir characterization, heterogeneity assessment, reservoir simulation, and the level of uncertainty in decision-making during field development planning. However, measuring permeability often involves expensive core analysis or well test analysis. It is typically not feasible to conduct such analysis across an entire reservoir involving cores from all wells. Therefore, there is a need to accurately model and predict permeability as a function of routinely obtained, lower cost, well logging data. Machine learning algorithms (ML) have been recently developed to reliably predict permeability by leveraging well logs data. In this research, an efficient tree-based (TB-ML) algorithm incorporating extreme gradient boosting (XGBoost) is employed to predict permeability in the Mishrif carbonate reservoir (Iraq) based on facies and well logging data. The recorded and interpreted well log variables used as input variables include gamma ray, caliper, density, neutron porosity, shallow and deep resistivity, total porosity, spontaneous potential, photoelectric factor, and water saturation. Additionally, core-derived permeability and porosity data is used to calibrate the ML predictions. The discrete reservoir facies are distinguished by applying a k-means clustering algorithm. Subsequently, the TB-ML algorithm is developed using the default and fine-tuned hyperparameters with the aid of two search algorithms: random search and Bayesian optimization. The permeability predictions are evaluated using cross-validation and error quantification metrics, which include the adjusted coefficient of determination (R2) and root mean squared error (RMSE). A comparison of adjusted-R2 and RMSE for the various TB-ML model configurations developed is compared for training and testing subsets to illustrate their permeability prediction performance. These results suggest that the method is sufficiently reliable to be generalized for application in both carbonate and clastic reservoirs in other oil and gas fields.