Identifying Technical and Economic EOR Potential Under Conditions of Limited Information and Time Constraints
Bibliographic record
Abstract
Abstract The application of Enhanced Oil Recovery (EOR) processes increases the economic value of existing fields through increased oil recovery and field life extension. This is especially important in depleted and mature fields, as oil production rates decline and water production increases. Additionally, environmental concerns also benefit EOR projects such as CO2 injection providing new opportunities for depleted and mature fields combined with a disposal of CO2 capturing emissions from industrial sources. Generally, oil companies (from majors to independents) operate a large field portfolio or a large number of reservoirs with lack of information. In some other cases, many reservoirs lack enough financial performance to justify information or data gathering. The latter represents a common barrier to identify appropriate investment opportunities in a particular or a portfolio of reservoirs. This paper describes an approach to perform an economic evaluation of EOR projects based on advance screening criteria and full field evaluations under conditions of limited information and time constraints. The proposed methodology estimates EOR technical viability and production potential in a particular field based on international field experience and fast evaluation tools such as analytical simulators and economic software programs. The proposed methodology not only provides a systematic approach for evaluating and ranking technical and economic EOR performance within a risk management framework but also shows an excellent potential to support decision-making processes in oil property acquisition and for the exploitation of unconventional reservoirs (CBM and Tight Gas), among others.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".