AI-Driven Petrophysical Interpretation of Subsurface Data for Reservoir Characterization: A Case Study from the Indus Basin
Bibliographic record
Abstract
Summary 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".