Solvent-Chamber Development in 3D-Physical-Model Experiments of Solvent-Vapour Extraction (SVX) Processes With Various Permeabilities and Solvent-Vapour Qualities
Bibliographic record
Abstract
Summary Solvent-vapour extraction (SVX) processes offer an attractive alternative to thermal recovery processes by being less energy intensive and are more suitable for thinner, partially depleted reservoirs. A typical SVX process uses solvent injection to dilute the heavy oil by reducing its viscosity, allowing it to be mobilized for production. During this process, the injection of hydrocarbon solvents results in partial deasphalting of the heavy oil, thus reducing its viscosity and enhancing the process performance further. This work examined the formation and growth of solvent chambers in laterally and vertically spaced horizontal injector/producer well pairs in porous media with five different permeabilities and three different solvent-vapour qualities. Consolidation of the porous media caused by asphaltene precipitation was also analyzed. Thermal-imaging and model excavation studies were performed to investigate the formation and growth of solvent chambers for seven different experiments conducted on a large 3D-physical-model apparatus. The important findings from this study are as follows: During solvent injection, one or more solvent fingers develop between the injector and producer. The dominant solvent finger becomes a conduit that grows into a solvent chamber connected to the injection well in the upper portion of the reservoir, and develops into an oil-drainage conduit connected to the production well in the lower portion of the reservoir. Solvent dispersion layers are located on the margins of both the solvent chambers and the oil-drainage conduits. The location and development of these nonuniform solvent chambers and oil-drainage conduits are unpredictable, and the oil-drainage conduits do not grow significantly in diameter once connected to the production wellbore, limiting the wellbore inflow efficiency and conformity. Asphaltene precipitation and migration can aggravate this inflow problem, reducing the SVX process performance further. SVX performance can be improved by increasing the number and diameter of oil-drainage connections between the solvent chamber and the production well, and by controlling the oil deasphalting process. This can be performed by optimizing injection- and production-wellbore geometries, and by optimizing solvent-injection rates and vapour quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".