Risk Analysis for Water Injection in a Petroleum Reservoir Considering Geomechanical Aspects
Bibliographic record
Abstract
Abstract Economic and risk analysis are important tasks in a petroleum engineering study. This work considers the analysis of the results obtained from a real Brazilian offshore field simulation study in which the decision making process for field development is discussed considering geomechanical aspects. The focus is on the evaluation of uncertainties of geomechanical parameters that may influence the response of a petroleum reservoir submitted to seawater injection for secondary recovery. A decision tree has been built and solved using utility theory and the expected monetary value (EMV) concept. A sensibility analysis for different discount rates and oil prices has been also performed. First, this work identifies if the reservoir is a candidate for a deeper analysis using a matrix approach consisting of important geomechanical parameters. Second, lab stress information is used and the results obtained compared with the traditional reservoir simulation, which considers compressibility invariant with time. Third, a decision tree is constructed and solved. Based on the assumptions chosen for these analyses, it is concluded that coupled reservoir-geomechanics simulations should be applied in order to more precisely forecast reservoir performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".