Upscaling Study of Cyclic Solvent Injection Process for Post-CHOPS Reservoirs through Numerical Simulation
Bibliographic record
Abstract
Abstract Cyclic Solvent Injection (CSI) is the most promising process for a post-CHOPS reservoir (Chang and Ivory 2012). Experimental studies suggest that oil recovery can reach up to 62% at the lab scale, which indicates the potential viability of the CSI process. This paper summarizes experimental results of six CSI tests with three physical models with different scales. Typical western Canadian heavy oil sample with a viscosity of 4830 cp at the reservoir conditions was used. Numerical simulation models were established to simulate these tests. The uncertainties in upscaling CSI process, such as relative permeability curve, capillary pressure, reaction rate in foamy oil model and dispersion coefficient were investigated by numerical simulation. In history-matched cases, the same relative permeability curves were obtained for these six CSI tests only with different capillary pressure. Sensitivity analysis illustrates that adding an appropriate capillary pressure in each test could refine the match results between simulation and experimental data. Generally, the larger the model is, the smaller the capillary pressure is. Therefore, the capillary pressure may be neglected in field scale applications. In addition, the location of the wormholes may affect the magnitude of capillary pressure employed in history-matched cases. A typical western Canadian heavy oil post-CHOPS reservoir (M-reservoir) was employed to study the uncertainties during the CSI process by numerical simulation. The uncertain parameters include oil relative permeability, gas relative permeability, capillary pressure and dispersion coefficient. The Design of Experiments (DOE) method was utilized to define the simulation matrix, and 18 simulation cases, with 7 factors in 3 levels, were run. The multiple inferences were covered as much as possible. The oil recovery factor for ten-year production was selected as the response variable. The range between 18 estimates and "standard" value (14.611%) in terms of the recovery factor was from 0.04% to 1.42%. After that, the multiple-linear regression was performed to construct the response surface in DOE and the proxy equations were then generated. Three thousand Monte-Carlo simulations, in total, were performed to generate the probability distribution functions, which indicated that the P90, P50 and P10 estimates of the oil recovery factors were 14.08%, 14.69% and 15.33%, respectively. This study demonstrates that through simulating experiments from physical models with different scales, the uncertainties in predicting the field-scale CSI performance can be significantly reduced.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".