Gasflooding-Assisted Cyclic Solvent Injection (GA-CSI) for Enhancing Heavy Oil Recovery
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 it