Experimental Study of Heavy Oil Recovery Mechanisms During Cyclic Solvent Injection Processes
Bibliographic record
Abstract
Abstract In recent years, the Cyclic Solvent Injection (CSI) process has shown to be a promising method for enhanced heavy oil recovery in Canada. CSI laboratory studies work for only 2 to 3 cycles due to low incremental oil in subsequent cycles. However, in field pilots the CSI keeps operational after many years. This study intends to capture the full production mechanisms responsible of heavy oil production in CSI to better understand the phenomena in field applications. A physical sandpack model was used to test the CSI response. The sandpack was saturated with live heavy oil of 7900 mPa.s viscosity at 24 ° C, and primary production was run. Five CSI tests were then conducted to simulate the performance under the gravity effects. The experiments were conducted in a horizontal and vertical mode injection respectively at high and low-pressure depletion rates using 70 mol % CH4 and 30 mol % C3H8 solvent mixture. The sandpack was Computed Tomography (CT) scanned after every cycle to provide information about the gas and oil saturations evolution. When CSI was run on the horizontal core, the incremental oil recovery was negligible for both slow and fast drawdown rates. When the sandpack was vertically flipped and rapidly produced, the three CSI cycles exhibited higher recover, and similar incremental recovery per cycle. This result indicated that even at high drawdown pressures, gravity segregation can effectively maximize the cross-flow mixing between solvent and heavy oil to penetrate the un-swept areas. The results of this study demonstrate the importance of gravity drainage in the CSI process, and the relative significance of gravity forces on successful oil recovery rates. The results of this study illustrate the limitations of previous horizontal laboratory tests and show an improved test configuration for modeling and prediction of the improved response observed in CSI pilots.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".