Integrated Method to Screen Tight Oil Reservoirs for CO2 Flooding
Bibliographic record
Abstract
Abstract As a result of poor fluid delivery in tight oil reservoirs, oil production drops rapidly at early stages of depletion development. While water flooding only boosts production to a limited extent, CO2 miscible flooding seems a promising technique in improving tight oil recovery. Generally, CO2 flooding is performed only after water flooding gives better results than natural depletion. Since cumulative CO2 injection versus oil production goes up as formation permeability goes down, it is crucial to select suitable reservoir candidates to conduct CO2 flooding to be economically successful. There are several methods of ranking candidate reservoirs for the CO2 enahnced oil recovery (EOR) process based on criteria on reservoir parameters. Nevertheless, few of them take account of an oil recovery increment and risk analysis. In this paper, an integrated method for CO2 flooding reservoir screening criteria is presented, considering asphaltene precipitation and an oil recovery factor increment. This method is based on the least squares method, reservoir simulation, and fuzzy analytical hierarchy process, associated with equation of state (EOS) compositional calculations and compositional modelling. It is applicable in high diversity and can be used as guidance to screen tight oil reservoirs for CO2 flooding.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".