Integrated Risk Analysis Criteria for Managing Exploration and Development of Oil and Gas Assets
Bibliographic record
Abstract
Abstract Current petroleum industry practice (as reported by Beucher et. al.1 and Bratvold and Begg2 etc. AAPG 2008) adopts schemes that aim at separating risk into two main categories; subsurface risk that includes resource size, production rate, and access cost and surface risk that demonstrates total expenditure, facilities delivery, delays, performance, oil/gas revenues and costs. A recent SPE review (Bartvold el al SPE RE 2009) of decision making in the petroleum industry shows great dependence on VOI (Value of Information), to manage risk. VOI cannot stand alone as a decision making tool. There are also shortcomings in the use of Monte Carlo simulation. In this paper, we introduce an integrated approach for handling risk associated with oil and gas exploration as well as development of mature reservoirs through EOR and IOR. Test case includes a major asset in the Arabian Gulf Region selected due to its varying reservoir settings. The proposed approach basically, integrates uncertainty elements that may create "business risk" causing "business impact". This solution methodology breaks down each risk parameter to sub-parameters. Critical and non critical sub-parameters are broken down to even more detailed pieces of risk components, down to the smallest fragments of risk. Thereafter, a down-to-top risk analysis technique is adopted to reach to target NCF (Net Cash Flow). Proposed is a tool for risk analysis modeling criteria related to exploration of oil and gas, and development of mature fields. In particular, it focuses on comprehension and inclusion and negates lumping/dropping of any risk parameter. This is not only because petroleum industry is surrounded by lots of uncertainties and therefore carries a huge risk, but also because other economies are revolving and highly dependent on this industry. In many cases, ignored risk parameters, that once thought non-critical, turned to business disasters.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".