Intelligent Interpretation of Sandstone Reservoir Porosity Based on Data-Driven Methods

To address the technical challenge of real-time interpretation of sandstone reservoir porosity during drilling, a data-driven approach is employed by integrating logging data with machine learning algorithms to deeply mine existing logging data and predict the porosity range of encountered reservoirs. Initially, the acquired logging data is cleaned, and correlation analysis is conducted on the feature parameters. Porosity values were discretized into intervals according to field conditions. Subsequently, porosity-intelligent interpretation models are established using One-vs.-One Support Vector Machines (OVO SVMs), Random Forest (RF), XGBoost, and CatBoost algorithms. Model parameters are optimized using grid search and cross-validation methods. Finally, the test data is interpreted based on the four models with optimized parameters. Results indicate that all four models achieve training accuracies exceeding 95% and test accuracies exceeding 85%. Considering precision, recall, and F1 score comprehensively, the RF model is selected for the case study, with all three indicators exceeding 96%. These findings demonstrate that data-driven methods based on machine learning can accurately interpret sandstone reservoir porosity within specified intervals. For porosity interpretation of sandstone reservoirs in different blocks, interpretation models should be developed using multiple machine learning algorithms, and the best performing model should be selected for practical deployment. This method can be integrated with geological steering drilling technology during horizontal well drilling to ensure that the wellbore trajectory passes through higher-quality reservoir intervals, thereby providing certain guidance for maximizing the encounter rate of reservoir sweet spots.