Effective EOR Decision Strategies With Limited Data: Field Cases Demonstration
Bibliographic record
Abstract
Summary Enhanced-oil-recovery (EOR) evaluations focused on asset acquisition or rejuvenation involve a combination of complex decisions using different data sources. EOR projects traditionally have been associated with high capital and operational expenditures (CAPEX and OPEX, respectively) as well as high financial risk, which tend to limit the number of EOR projects launched. We propose a workflow for EOR evaluations that accounts for different volumes and quality of information. This flexible workflow has been applied successfully to oil-property evaluations and EOR-feasibility studies in many oil reservoirs. The method associated with the workflow relies on traditional (e.g., look-up tables, x-y correlations) and more-advanced (data mining for analog-reservoir search and geology indicators) screening methods, emphasizing identification of analogs to support decision making. The screening phase is combined with analytical or simplified numerical simulations to estimate full-field performance with reservoir-data-driven segmentation procedures. This paper illustrates the EOR decision-making workflow by use of field case examples from Asia, Canada, Mexico, South America, and the United States. The assets evaluated include reservoir types ranging from oil sands to condensate reservoirs. Different stages of development and information availability are discussed. Results show the advantage of a flexible decision-making workflow that can be adapted to the volume and quality of information by formulating the correct decision problem and concentrating on projects and/or properties with the highest expected economic merit. An interesting aspect of this approach is the combination of geologic and engineering data, minimizing experts' bias and combining technical and financial figures of merit. The proposed method has proved useful to screen and evaluate projects/properties very rapidly, identifying when upside potential exists.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".