An Innovative Method: Risk Assessment for Exploration and Development of Oil and Gas
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 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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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 | 0.000 | 0.000 |
| 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