Post-Cold Production Solvent Vapor Extraction (SVX) Process Performance Evaluation by Numerical Simulation
Bibliographic record
Abstract
Abstract In the classical Vapex (Vapor Extraction) process, a horizontal injector is stacked vertically, within a few meters, above a horizontal producer to utilize gravity to facilitate the drainage of the solvent-diluted oil to the producer. The fundamental challenge with Vapex is that the oil rate in the field is far too low to allow a commercially viable process. In recent years, researchers have proposed the solvent vapor extraction process (SVX) after cold production, in which the injector/producer pair is spaced laterally. The wormholes between the injector and the producer formed during cold production are utilized as conduits available to produce diluted oil and enhance the oil rate. This paper reports the simulation results on the effects of wormhole characteristics (length, permeability and water saturation, direction, and wormhole branch number) and fluid and formation uncertainties (dispersion coefficients, relative permeability curves and reservoir heterogeneity) on the production performance. Results suggest that (1) using the wormholed well as a solvent injector has better performance than using the wormholed well as a producer; (2) the water saturation in the wormhole is crucial for the successful application of the post-cold production SVX process; (3) The upper most wormhole has a larger influence on the solvent injection performance than the lower wormholes; (4) The reservoir and fluid uncertainties, such as relative permeability curves, dispersion coefficients and reservoir heterogeneity cansignificantly reduce the oil flow rate and increase the time for solvent breakthrough and make this process uneconomical; (5) on the basis of the simulation, randomized permeability heterogeneity distribution is equivalent to a homogeneous model. However, if permeability continuity is considered, the reservoir heterogeneity has significant effects on the SVX performance. Introduction In Canada, there are an estimated 30 billion barrels of heavy oil in place, of which only about 12% can be economically recovered with current technology; about 26 billion barrels are considered unrecoverable. Early water breakthrough and high water cuts due to unfavorably high water-oil mobility ratios have resulted in poor recoveries, and many thin heavy oil reservoirs have not been developed due to the uncertain economics. Solvent Vapour Extraction (SVX) process show the most promise in producing heavy oil from thin reservoirs in Saskatchewan(1). Many experimental and simulation studies(2–7) have been conducted to investigate the performance of SVX processes. However, most of those studies focused on injecting solvent into un-produced reservoirs. Without considering the change in initial conditions provided by primary or secondary production techniques, the results of the studies only reflected the performance of solvent injection. However, it is expected that SVX processes will be used in the field as a post-cold production enhanced oil recovery method(1,8). Generally the mechanisms of cold production are divided into two main categories: development of wormholes and foamy oil flow. The post-cold production solvent injection may benefit from both of the cold production mechanisms. The wormholes developed during cold production may provide a fast conduit for solvent reaching the crude oil far away from the solvent injector.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".