A machine-learning-based approach to predict potential oil sites: Conceptual framework and experimental evaluation
Bibliographic record
Abstract
Abstract Petroleum exploration requires careful planning due to high risks, expenses, and time consumption. Predicting oil sites is the preliminary step in petroleum exploration. Oil site prediction can be optimized by machine learning (ML) algorithms and techniques. This article highlights the issue of optimizing oil site prediction, which has not been focused on in existing research studies. The article proposes an approach that aims to improve the prediction of potential oil sites. The proposed approach is based on a conceptual framework and an assessment of geological criteria. The criteria are assessed and validated based on a sensitivity analysis. The framework is based on convolutional neural networks (CNNs), a subsidiary of ML, and image processing techniques. It consists of four main steps, which are data preparation, semantic segmentation, model training, and validation. The framework is implemented in a case study in the Fort MacKay region of Alberta, Canada. The implementation of the proposed approach shows promising accuracy for predicting potential oil sites, with a 95% intersection between the predicted potential zone and the actual petroleum site. The main contribution of the proposed approach is a sensitivity assessment of geologic criteria and a CNN-based model to enhance the prediction of potential petroleum sites.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".