New Experimental Model Design for Systematic Investigation of Capillarity and Drainage Height Roles in Vapor Extraction Process
Bibliographic record
Abstract
Abstract Vapor Extraction is a solvent-leaching gravity drainage-based process that can be used for improving recovery factors from0020low-pressure heavy oil and bitumen reservoirs. Despite numerous experimental and numerical studies having been conducted, the associated production mechanisms in this process are still poorly understood. This paper presents the details of a new experimental approach that can overcome limitations of previous physical models for representative simulation of capillarity and drainage height effects on performance of the vapor extraction process. Unlike other studies in literature, the physical models in this study were designed to provide a constant composition solvent surface at one face of the heavy oil saturated sandpack. To eliminate the effect of pressure surges, a new effluent collection system was successfully designed and tested. This experimental setup made it possible to study the interplay between the selected parameters on stabilized drainage rates under effects of capillary and gravity forces. Here, a comprehensive experimental study was conducted in two 2D visual physical models using Plover Lake oil from west-central Saskatchewan and n-butane as solvent in the permeability range of 5.1–6.5D. This paper discusses the significant effects of capillarity and drainage height on the stabilized drainage rates, Vapex dimensionless number, and efficiency of mass transfer phenomena in the Vapex process. This study provides in-depth knowledge necessary for developing more reliable mass transfer models to depict the vapor extraction process in the presence of capillary forces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".