Importance of a Second Liquid Phase Formation in CO2 Injection into Bitumen Reservoirs and its Effect on Production
Bibliographic record
Abstract
Abstract The injection of different solvents, such as propane and CO2, into bitumen, has proven to be an effective method in the production of these kinds of reservoirs. However, in some cases, the prediction of large solvent requirements can make it uneconomical. The formation of a second liquid phase has been observed when the solvent is propane or CO2, with the second liquid phase mainly composed of the solvent itself. The objective of this research is to understand the importance of this second liquid phase and its effect on production. Also, a simulator that can allocate an individual phase to this liquid phase would allow for prediction of the amount of solvent that can be produced and recycled. This makes the cost evaluation of solvent injection processes to be more realistic. Depending on the reservoir fluid distribution, a three- or four-phase flow can occur in the absence or presence of water. A compositional simulator based on an equation of state is designed to simulate these multiphase situations. This simulator has a four-phase flash and stability subroutine, which make it more realistc compared to other compositional simulators. In fact, it can handle a maximum of three hydrocarbon phases and one aqueous phase. Relative permeability plays an important role in multiphase flow; numerical results indicate that, by increasing the number of phases, there is an increase in project life. It is valuable to mention that the results of this research can be also used in CO2 sequestration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".