Artificial Intelligence…as a Decision Support System for Petroleum Engineers
Bibliographic record
Abstract
Artificial intelligence (AI) has drawn the attention of many researchers over the last two decades. It is gaining popularity at a rapid pace. The main interest in AI has its roots in the recognition that the human brain processes information at much slower rate than computer gates, yet human brains are more efficient than computers at computationally complex tasks such as understanding speech and other pattern recognition problems. In the oil and gas field, AI can help engineers and researchers to overcome difficulties by addressing some fundamental problems (such as the determination of formation permeability from well logs) or specific problems (such as forecasting postfracture well-performance in the absence of engineering data), which conventional computing has been unable to solve. This paper presents a historical overview of the advancement of AI systems along with presentation of the various systems developed over the last decade. A detailed discussion of the importance of AI as a valuable tool in the petroleum industry is presented. The various mechanisms by which AI achieves its objective are also discussed. The main goal of this paper is to put Artificial Intelligence in perspective from the point of view of petroleum engineering and encourage engineers and researchers to consider it as a valuable alternative tool in the petroleum industry.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".