Effective EOR Decision Strategies with Limited Data: Field Cases Demonstration
Bibliographic record
Abstract
Abstract Enhanced-Oil Recovery (EOR) for asset acquisition or rejuvenation involves intertwined decisions. In this sense, EOR operations are tied to a perception of high investments that demand EOR workflows with screening procedures, simulation and detailed economic evaluations. Procedures have been developed over the years to execute EOR evaluation workflows. We propose strategies for EOR evaluation workflows that account for different levels of available information. These procedures have been successfully applied to oil property evaluations and EOR applicability in a variety of oil reservoirs. The methodology relies on conventional and unconventional screening methods, emphasizing identification of analogues to support decision making. The screening phase is combined with analytical or simplified numerical simulations to estimate full-field performance while maintaining rational reservoir segmentation procedures. This paper fully describes the EOR decision-making procedures using field case examples from Asia, Canada, Mexico, South America and the United States. The type of assets evaluated includes a spectrum of reservoir types, from oil sands to light oil reservoirs. Different stages of development and information availability are discussed. Results show the advantage of flexible decision-making frameworks that adapt to the volume and quality of information by formulating the correct decision problem and concentrate on projects and/or properties with apparent economic merit. Our EOR decision-making approaches integrate several evaluation tools, publicly or commercially available, whose combination depends on availability and quality of data. The decision is laid out using decision-analysis tools coupled with economic models and numerical simulation. This allows integrated teams to collaborate in the decision making process without over-analyzing the available data. One interesting aspect is the combination of geologic and engineering data, minimizing experts’ bias and combining technical and financial figures of merit rationally. The proposed methodology has proved useful to screen and evaluate projects/properties very rapidly, identifying whether or not upside potential exists.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".