Experimental and Theoretical Study of Operating Pressure and Capillary Pressure on Vapor Oil Gravity Drainage VOGD in Fractured Reservoirs
Bibliographic record
Abstract
Abstract The Vapor-Oil Gravity Drainage (VOGD) is a low temperature, solvent-enhanced gas-oil gravity drainage (GOGD) process targeting naturally fractured viscous reservoirs. The experimental set up and corresponding acquired data was previously introduced by the authors (Anand et al., 2017) in which the effects of temperature, solvent injection rate, and solvent type (n-Butane and dichloromethane (DCM)) were investigated. Results from Anand et al. work indicated encouraging high oil rates and ultimate recoveries; results also demonstrated that the oil rates and recovery were impacted by diffusion and dispersion (in the form of intrinsic gas rate), asphaltene precipitation, and capillary pressure. The intent of this work is to further study the mechanisms behind VOGD; in particular those related to operating pressure and solvent vapor-oil capillary pressure. The results from this work show that the ultimate recovery and oil rate are positively correlated to the operating pressure; experiments conducted at 50% and 75% saturation pressure (Psat) yielded lower ultimate oil recoveries, ranging from 33% to 68% of original oil in place (OOIP), when compared to the experiments conducted at 90% Psat (70% of OOIP). Moreover, n-butane performed better than DCM and lesser asphaltene precipitation was seen at lower Psat. The main drivers for these observations were found to be lower solvent solubility and larger capillary pressure values at lower values of Psat.
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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 | 0.001 |
| 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".