An Innovative Method: Risk Assessment for Exploration and Development of Oil and Gas
Bibliographic record
Abstract
Abstract This paper presents a new method and flowchart of risk assessment for the oil and gas upstream industry to identify and evaluate the risks in the oil and gas investment activities. It integrates several commonly used risk identification techniques, including fault tree analysis, brainstorming, and Delphi and event tree analysis methods. This paper divides the risk factors into three categories: social environment, natural environment and resources, technology and management, there are several risk factors in each category; therefore, a risk assessment system of the three hierarchies is built up. In the third hierarchy, a risk grade standard is established according to expected economic loss, which is determined by the risk probability and the consequence of the risk factors. A new method of the risk assessment was presented, Fuzzy Analytical Hierarchy Process (Fuzzy-AHP). The integrated risk grade of oil and gas exploration and development project can be obtained. In this method, the influence weight and the consequence of risk factors are comprehensively considered. It gives a significant reference for decision-makers of the oil and gas upstream industry. At present, this method has been recommended to the exploration and development risk assessment projects in the oilfields, China.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".