Experimental Study of Heavy Oil Recovery Mechanisms during Cyclic Solvent Injection Processes
Bibliographic record
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 whereas field pilots continue for years over multiple cycles This experimental study is intended to capture the production mechanisms responsible for heavy oil production in CSI. Primary production and CSI tests were conducted using physical sandpack models saturated with live heavy oil of 9,530 mPa.s viscosity. The experiments were conducted in horizontal and vertical mode injection at high- and low-pressure depletion rates using two solvent mixtures of CH4 and C3H8. The sandpack was Computed Tomography scanned after every cycle to analyze the evolution of gas and oil saturations. Three cores were used to study the effect of gravity forces, depletion rate, solvent composition, and initial oil saturation (dead/live oil systems) on the performance of CSI processes. CSI after primary in horizontal systems produced negligible incremental oil for both slow and fast drawdown rates due to the large void space and high free gas saturation inhibiting the pressure build up to push the solvent-diluted oil. These CSI experiments were only successful in dead oil systems, where the initial oil saturation was high and pressure gradient was generated through fast depletion rates until conditions of high void space and gas channels were reached. When the sandpack was flipped vertically, CSI cycles exhibited higher incremental oil recovery per cycle. Slow depletion cycles were more efficient in terms of pressure and incremental recovery per cycle, however, faster depletion cycles performed better as a function of time. The higher C3H8 content solvent mixture exhibited better performance in comparison to the lower C3H8 content as higher volume of diluted oil was drained out of the core. These results demonstrate the importance of gravity drainage in the CSI process and its significance on successful oil recovery rates. This study illustrates the limitations of previous horizontal laboratory tests and shows an improved test configuration for modelling and prediction of the improved response observed in CSI pilots
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".