Experimental and Numerical Studies of the Reservoir Permeability and Drainage Height Effects on the Solvent-Diluted Heavy Oil Gravity Drainage
Bibliographic record
Abstract
Abstract Solvent-based enhanced heavy oil recovery techniques are promising alternatives to thermal-based methods. A better understanding of the solvent-diluted heavy oil gravity drainage is important to practical field-scale applications of the solvent-based heavy oil recovery processes. In particular, the transmissibility, which is defined as the product of the absolute permeability k and pay-zone thickness h of an oil reservoir, is an important parameter to affect the solvent-diluted heavy oil gravity drainage. In this paper, a series of experimental tests were conducted in a visual high-pressure physical model to study the permeability effect. The physical model was packed with the Ottawa sands of different sizes to obtain different absolute permeabilities in the range of k = 7–54 Darcy. The original heavy oil sample collected from the Lloydminster area in Canada was used to saturate the sand-packed physical model and pure propane was used to extract the heavy oil. The drainage height effect was studied by performing the solvent-diluted heavy oil gravity drainage tests in another visual physical model with a different drainage height. The produced heavy oil and solvent were collected and recorded in each test. The produced oil samples obtained at different times were flashed and the flashed-off heavy oil viscosities were measured. It was found that heavy oil was produced much faster in a higher permeability physical model with a larger drainage height. A new correlation was developed to take account of the reservoir permeability and drainage height effects (i.e., the transmissibility effect) on the heavy oil production. In addition, the average produced solvent–oil ratio (SOR) was determined to be in the range of 0.3–0.6 g solvent/g heavy oil. The average SOR was increased as the reservoir permeability and drainage height were decreased. It was also found that the produced heavy oil viscosity was reduced significantly. Furthermore, numerical simulations were undertaken by using CMG STARS module to match the experimental results. This study provides much-needed physical and practical understanding of the solvent-based heavy oil recovery.
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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 | 0.000 |
| 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".