Feasibility Study of Solvent-Based Huff-n-Puff Method (Cyclic Solvent Injection) to Enhance Heavy Oil Recovery
Bibliographic record
Abstract
Abstract Solvent-based processes have demonstrated a significant potential to enhance heavy oil recovery. However, their applicability needs to be investigated for different solvents and operating conditions. In this study, a comprehensive experimental and reservoir simulation analysis was conducted on the feasibility of solvent-based, huff-n-puff method to enhance heavy oil recovery. Carbon dioxide (CO2), methane (CH4), propane (C3H8), and butane (C4H10) were tested under different operating conditions. A physical model with a1800-md permeability and 24% porosity Berea core mounted in a high-pressure core holder was designed. For all tests, the core was saturated with a Saskatchewan heavy oil with viscosity of 1423 mPas at 22 °C. Fourteen huff-n-puff experiments were conducted. The effect of operating pressure, soaking time, and solvent composition were investigated. According to the results, for all types of solvent the produced oil at elevated pressure was lighter (in terms of density and viscosity) and the recovery factor was higher. The highest recovery of 71% was obtained by injecting pure CO2 at near-supercritical conditions (7239 kPa at 28 °C), while pure CH4 at the highest operating pressure of 6895 kPa was 50%. Also, adding 19% hydrocarbon solvent to pure CO2 increased the recovery factor by 10% at aoperating pressure (e.g., 2317kPa). The governing mechanisms that contributed to the production were recognized to be solution gas drive, viscosity reduction, extraction of lighter components, formation of foamy oil, and to a lesser degree, the diffusion process. The oil viscosity was reduced to 62 mPas by injecting CO2 at 7239 kPa. The highest incremental recovery for CO2-based solvents and CH4 occurred at the 2nd and 3rd cycle, respectively. Longer soaking time improved the incremental recovery of the first cycles, though the final recovery did not noticeably change. The result of history matching with the simulated model was quite reasonable with maximum 10% discrepancy between recovery factors of these two approaches.
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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 | 0.000 |
| 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) | 0.001 | 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".