Enhanced Vuggy Zone Recognition Through Merging AI-based Borehole Image Interpretation

Abstract Recognizing vuggy zones in borehole imaging (BHI) presents significant challenges due to the intricate and heterogeneous nature of these geological features (fig.1). Traditional methods, which often rely heavily on subjective visual interpretation of BHI data, are prone to variability and inconsistencies. These limitations contribute to substantial uncertainties in geological modeling and subsurface characterization, impacting the reliability of reservoir assessments. The integration of AI-driven interpretation techniques offers a transformative solution to these challenges. By leveraging advanced machine learning algorithms, we can systematically analyze high-resolution borehole imaging data, reducing the dependence on human interpretation and minimizing associated biases. These algorithms are capable of identifying subtle patterns and features indicative of vuggy zones that traditional methods might overlook or misinterpret. AI enhances the precision and consistency of vuggy zone identification, facilitating a more rigorous quantification of uncertainties inherent in these interpretations. This not only strengthens the robustness of geological models but also leads to more accurate predictions of reservoir properties and behavior. As a result, critical decision-making processes in subsurface characterization—such as reservoir management, well placement, and production forecasting—are significantly improved. This optimization of resource extraction efforts directly translates to reduced operational risks and enhanced economic outcomes.