Artificial intelligence applications and challenges in oil and gas exploration and development
Bibliographic record
Abstract
The rapid integration of artificial intelligence into oil and gas exploration and development offers transformative opportunities within the context of the global energy transition. This article highlights the key advancements and challenges in artificial intelligence applications. Machine learning algorithms enable data-driven shale sweet spot prediction, overcoming the limitations of traditional methods by capturing complex controlling factors. Intelligent core image analysis, leveraging computer vision and foundation models, enables automatic mineral identification, pore analysis, and rock structure characterization, thereby providing a comprehensive framework for microscopic reservoir appraisal. Physics-informed neural networks address the limitations of purely data-driven reservoir simulation by embedding governing seepage equations into their loss functions, thereby ensuring physical consistency and improved generalization. Multimodal architectures significantly enhance unconventional shale gas production prediction by integrating geological heterogeneity with dynamic production behavior, leading to more accurate and stable forecasts. Collectively, these AI-driven approaches underscore the importance of combining domain expertise, multi-source data, and physics-aware modeling to achieve efficient and intelligent oil and gas development. Document Type: Perspective Cited as: Hui G., Ren Y., Bi J., Wang M., Liu C. Artificial intelligence applications and challenges in oil and gas exploration and development. Advances in Geo-Energy Research, 2025, 17(3): 179-183. https://doi.org/10.46690/ager.2025.09.01
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".