Accurate prediction of porosity and permeability in complex carbonate reservoirs is very important for understanding reservoirs, but remains challenging due to inherent heterogeneity. This study develops a robust, machine learning-driven workflow to enhance the prediction of these critical petrophysical properties and the identification of Hydraulic Flow Units. The methodology integrates conventional core data and geophysical well logs, employing advanced data preprocessing, including depth matching, which significantly improved the log-core porosity correlation. A key innovation involves using a Gaussian Mixture Model for unsupervised Hydraulic Flow Unit identification, which outperformed traditional empirical methods and K-Means clustering by yielding five distinct Hydraulic Flow Units with high intra-unit porosity–permeability correlations (R 2 up to 0.93) validated by Mercury Injection Capillary Pressure data. For predictive modeling, a comprehensive comparison of algorithms revealed that a Voting ensemble meta-algorithm with a Multi-Layer Perceptron base learner delivered superior performance for both porosity (on integrated data from three wells) and permeability (modeled per Hydraulic Flow Unit). The final models successfully estimated properties in non-cored intervals and a blind well, demonstrating high accuracy and generalizability. This integrated approach provides a reliable and theory-grounded framework for characterizing heterogeneous carbonate reservoirs, reducing dependency on extensive coring operations.
Panah et al. (Fri,) studied this question.