Bibliographic record
Abstract
Abstract Screening for Enhanced Oil Recovery (EOR) processes is a critical step in evaluating future development strategies for depleted reservoirs under primary and secondary recovery. However, selecting the optimum EOR process for a given reservoir is challenging because it requires evaluating and comparing performance for various EOR processes, which is complex and time consuming. This paper presents a new EOR screening model that can predict the performance of various gas- and water-based EOR processes based on simple reservoir properties. The model estimates the oil recovery from miscible and immiscible gas/solvent injection (CO2, N2, and hydrocarbons), low salinity water flood, polymer, surfactant-polymer, alkaline-polymer and alkaline-surfactant-polymer floods. The screening model is based on a set of correlations that were developed using the response surface methodology, which correlates the oil recovery at dimensionless times to the important reservoir and fluid properties and EOR process variables identified for each process. The results of the model have been validated against a number of field test and numerical simulation results. The screening model provides the capability to screen a large set of reservoirs for a wide spectrum of EOR processes, to identify the good EOR targets and the optimum EOR process for the target reservoirs. In addition, this model easily performs sensitivity analysis without the need for numerical simulations, allowing teams to account for uncertainty in reservoir properties and optimization of flood design. Finally, the methodology can be applied for developing screening models for other oil recovery mechanisms such as thermal (steam injection, SAGD), microbial EOR and other methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".