Experimental and Theoretical Study of Rate, Temperature and Solvent-Type Effects on Vapor Oil Gravity Drainage VOGD in Fractured Reservoirs
Bibliographic record
Abstract
Abstract Application of thermal and solvent EOR technologies for heavy oil recovery in naturally fractured reservoirs is generally challenging due to low permeability, unfavorable wettability and mobility, and considerable heat losses. Vapor-Oil Gravity Drainage (VOGD) is a modified low temperature, solventonly injection technology, targeted at improving heavy oil recovery in fractured reservoirs. It utilizes high fluid conductivity in vertical fractures to establish large solvent-oil contact area rapidly and eliminates the need for massive energy and water inputs as compared to thermal processes, by operating at significantly lower temperatures with no water requirement. Investigation of the effect of solvent injection rate, temperature and solvent-type (n-Butane and dichloromethane (DCM)) on the recovery profile was carried out on a single-fracture core model. This work combines the knowledge obtained from experimental investigation and analytical modeling using Butler's correlation with validated fluid property models to understand the relative importance of various recovery mechanisms behind VOGD, namely molecular diffusion, asphaltene precipitation and deposition, capillarity, and viscosity reduction. Experimental and analytical model studies indicated that molecular diffusion, convective dispersion, viscosity reduction via solvent dissolution and gravity drainage are dominant phenomena in the recovery process. Material balance analysis indicated limited asphaltene precipitation. High fluid transmissibility in the fracture along with gravity drainage led to early solvent breakthroughs and oil recoveries as high as 75% OOIP. Injecting butane at higher rate and operating temperature enhanced vapor solvent rate inside the core leading to highest ultimate recovery. Increasing operating temperature alone did not improve ultimate recovery due to decreased solvent solubility in the oil. Although with DCM, lower asphaltene precipitation should maximize oil recovery rate, its higher solvent (vapor)-oil interfacial tension resulted in lower ultimate recovery than butane. Ideal density and non-ideal double-log viscosity mixing rules along with molecular diffusivity as a power function of oil viscosity were used to obtain an accurate physical description of the fluids for modeling solvent-oil behavior. With critical phenomena such as capillarity and asphaltene precipitation missing, Butler's analytical model under predicts recovery rates by as much as 70%.
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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".