Effect of Pressure on Vapor/Oil Gravity Drainage in Fractured Reservoirs
Bibliographic record
Abstract
Summary This fundamental research is part of a larger study in determining the capability of a solvent process, referred to as vapor/oil gravity drainage (VOGD), for enhancing gravity drainage of viscous oil in fractured reservoirs by the injection of solvent. The solvent can be designed to traverse the reservoir mostly in its vapor phase at the reservoir temperature and pressure. Heated solvent vapor can also be used to facilitate the propagation of solvent vapor in low-temperature reservoirs, taking advantage of both thermal- and solvent-recovery processes. The experimental setup and corresponding acquired data were previously introduced by the authors in Anand et al. (2018), in which the effects of temperature, solvent-injection rate, and solvent type [n-butane and dichloromethane (DCM)] were investigated. Results from Anand et al. (2018) indicated encouraging high oil rates and ultimate recoveries; results also demonstrated that the oil rates and recovery were affected by diffusion and dispersion (in the form of intrinsic gas rate), asphaltene precipitation, and capillary pressure. The intent of our present 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), compared with the experiments conducted at 90% Psat (recovery of 70% OOIP). Moreover, n-butane performed better than DCM, and lower 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 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 | 0.001 | 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 | 0.001 |
| 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".