What question did this study set out to answer?

The study aims to enhance prediction accuracy of physical properties in carbonate mound-shoal complexes using machine learning techniques.

April 22, 2026Processes0 citationsOpen Access

Machine Learning-Based Optimization for Predicting Physical Properties of Mound–Shoal Complexes

PHPeiran Hao GCGongyang Chen YNYi Ning

Key Points

The study aims to enhance prediction accuracy of physical properties in carbonate mound-shoal complexes using machine learning techniques.
Evaluated six machine learning models: SVM, BPNN, LSTM, KNN, RF, and GPR.
Used seven logging parameters as inputs to predict porosity and permeability.
Integrated geological knowledge and applied cross-validation to optimize models.
RF model achieved an R² of 0.6824 in predicting permeability.
GPR model provided an R² of 0.7342 and an Accuracy Index of 0.9699 for porosity estimation.
Machine learning models struggled with accurately identifying low-permeability zones in heterogeneous reservoirs.

Abstract

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional empirical methods. This study investigates the application of machine learning algorithms for optimizing the prediction of reservoir properties in hill-and-plain carbonate bodies. Six machine learning approaches—Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), K-Nearest Neighbors (KNN), Random Forests (RF), and Gaussian Process Regression (GPR)—are systematically evaluated and compared. The analysis employed flow zone indices, geological data, and well log curves to classify porosity–permeability types. Seven logging parameters were used as input features: spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), bulk density (RHOB), acoustic travel time (DT), neutron porosity (NPHI), and true resistivity (RT). These features were paired with measured physical property values to train and validate the predictive models. Results demonstrate distinct algorithmic advantages for specific properties. The RF model achieved superior performance in permeability prediction, yielding an R2 of 0.6824, whereas the GPR model provided the highest accuracy for porosity estimation, with an R2 of 0.7342 and an Accuracy Index (ACI) of 0.9699. Despite these improvements, machine learning models still face limitations in accurately characterizing low-permeability zones within highly heterogeneous hill–terrace reservoirs. To address this challenge, the study integrates geological prior knowledge into the machine learning framework and applies cross-validation techniques to optimize model parameters, thereby providing a practical and robust approach for detailed assessment of mound–hoal carbonate reservoirs.

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