Effect of Dead Oil Viscosity and Injected Solvent Type on SVX Process Performance
Bibliographic record
Abstract
Abstract This work presents experimental results of four 3D physical model experiments that were performed to evaluate the dependence of solvent vapour extraction (SVX) recovery process performance on initial dead oil viscosity and injected solvent mixture composition. Model excavation studies were also performed to approximate the solvent movement in the physical model and to map out the residual oil saturation and precipitated asphaltenes. All of the four experiments — designated as SVX 14, 15, 16 and 17A — were performed on a 3D physical model (having permeability and porosity of ~4 darcy and ~33%, respectively) that was oriented vertically to provide model dimensions of 2.5 m thick by 0.2 m wide by 0.5 m long. The 0.5-m-long horizontal top injection and bottom production wells were separated laterally across the width and vertically across the thickness. Prior to solvent injection the model was resaturated with live oil containing methane at a gas/oil ratio ranging from 8.5 to 11 sm3/sm3. The initial dead oil viscosity at 21°C for SVX 14, 15, 16 and 17A were approximately 13,000, 33,000, 16,000, and 48,000 mPa•s, respectively. The composition of the injected solvent mixture for SVX 14 and 15 was ~57/43 mol% of methane (C1)/propane (C3) and for SVX 16 and 17A was ~57/43 mol% of carbon dioxide (CO2)/propane (C3). The analysis of the experimental results revealed that the initial dead oil viscosity had a significant effect on the SVX performance. Higher oil production rates were achieved with the lower viscosity oils for both injected solvent mixture types, with a more pronounced effect observed when using the CO2/C3 solvent mixture. The results also showed that the CO2/C3 mixture resulted in earlier solvent breakthrough and initial oil production, reduced solvent makeup requirements (i.e., better solvent recycle stream), and reduced solvent retention in the model/reservoir, as compared to the C1/C3 solvent mixture. The residual oil saturation mapping showed that the CO2/C3 mixture led to comparatively lower values in the drained regions and higher amount of precipitated asphaltenes remaining in the model, although there was evidence of some precipitated asphaltenes close to both the injection and production wells in all four experiments, regardless of the solvent mixture type. Finally, this mapping also indicated that the solvent/oil interfaces and solvent chambers were more uniform and predictable for the CO2/C3 solvent mixture injection. The field scale application and optimization of SVX processes requires that relationships be established among oil production rates, solvent utilization, and important process parameters such as matrix permeability, initial oil viscosity, solvent mixture composition, solvent utilization, etc. This work has attempted to generate such relationships for the two most promising mixed solvent systems being considered for use in heavy oil reservoirs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".