Gasflooding-Assisted Cyclic Solvent Injection (GA-CSI) for Enhancing Heavy Oil Recovery
Bibliographic record
Abstract
Abstract Cyclic solvent injection (CSI) process takes advantage of solution-gas drive and foamy oil flow for the oil production. However, it suffers from the solvent liberation during the production period. This results in an increased viscosity of the oil and its mobility loss. 0How to recover the partially diluted heavy oil becomes a key challenge for a CSI process. This paper first experimentally studies the conventional CSI processes with a one-well configuration, in which the solvent injector is alternately used the oil producer, and a two-well configuration, in which the solvent injector and oil producer are placed horizontally apart. It is found that during the one-well CSI test, some foamy oil that remains in the solvent chamber at the end of the production period of a previous cycle is pushed back by the injected solvent during the injection period of the next cycle. Such a back-and-forth movement of oil is not observed in the two-well CSI test. In addition, it is found that the oil saturation and oil relative permeability inside the solvent chamber are increased due to the foamy oil flow during the production period. Based on this fact, a new process, namely gasflooding-assisted cyclic solvent injection (GA-CSI), is proposed to enhance the performance of the CSI process. In this new process, a gasflooding slug is applied after the pressure depletion process to produce the partially diluted foamy oil in the solvent chamber. Results show that the GA-CSI process can increase the oil production rate by over 3 times, in comparison with the conventional CSI process.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".