Further Investigation of Drainage Height Effect on Production Rate in Vapex
Bibliographic record
Abstract
Abstract Efficient recovery of heavy oil and bitumen is still very challenging and remains a subject of ongoing researches all around the world. Thermal recovery methods are generally accepted as viable and several successful field projects based on CSS or steam flooding or SAGD have been reported. However, these processes are not universally applicable and there are many problematic heavy oil reservoirs that are not good candidates for thermal processes. Solvent based non-thermal heavy oil recovery methods are relatively new and still under development. The central idea in such techniques is to rely on solvent dissolution for viscosity reduction instead of heating. The viscosity reduction effect of solvents is comparable to that of steam. Among the solvent based methods, Vapex appears to be the most promising. Vapex is the solvent analogue of the SAGD process and uses essentially the same well configuration. A solvent vapor is injected instead of steam and the viscosity reduction occurs due to the dissolution and diffusion of the solvent in the oil. In thin heavy oil and bitumen reservoirs, where the thermal processes are likely to fail due to excessive heat losses, Vapex can be more successful. Nonetheless, the economic viability of Vapex remains uncertain due to much lower oil production rates predicted by scale-up of observed laboratory model results using the transmissibility based scaling criteria developed by Butler1. In our previously reported experimental work, it was shown that the currently used scaling methods under-predict the increase in oil production rate with increasing drainage height. A bigger height dependency of the oil rates than the square root functionality proposed by the Butler's model1 was found experimentally. This observation was based on physical models of four different sizes, the largest of which was only 60 cm tall. In this paper, the results of a new set of Vapex experiments are presented. This set of experiments has been carried out in a newly designed very large physical model, which to the best of authors’ knowledge is the largest Vapex experimental model ever fabricated. The results are, interestingly, in very good agreement with the trend of the previous experiments with the smaller models. The previously proposed scale-up relationship2 adequately predict the results obtained with the new physical model. The new results have been used to improve the previously reported empirical scale up correlation for the Vapex process. Conducting the experiments with different sand packs also reconfirms square root functionality of the dead oil production rate to the permeability.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".