Artificial Intelligence…as a Decision Support System for Petroleum Engineers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it