AI-Driven Petrophysical Interpretation of Subsurface Data for Reservoir Characterization: A Case Study from the Indus Basin

This study presents an AI-driven approach to petrophysical interpretation for enhanced reservoir characterization, using subsurface data from the Indus Basin, Pakistan. Traditional petrophysical analysis methods often face challenges related to data quality, non-linearity, and complex reservoir heterogeneity. To overcome these limitations, this research integrates Artificial Intelligence (AI) and Machine Learning (ML) techniques to predict key reservoir properties including porosity, water saturation, Lithofacies and shale volume.

Well log and, where available, was preprocessed, normalized, and used to train and test various ML models. Model performance was evaluated using standard statistical metrics such as R², RMSE, and MAE to ensure reliability. The results demonstrated that AI-based models significantly improve prediction accuracy compared to traditional empirical methods, especially in complex lithological zones.

The case study from the Indus Basin highlights the applicability of ML algorithms in identifying sweet spots, improving reservoir quality mapping, and supporting better decision-making in exploration and development planning. This research contributes to the growing body of work focused on digital transformation in the energy sector and showcases the potential of AI/ML in modern petroleum engineering workflow.