Dynamic Modelling of Solution-Gas Drive in Heavy Oils
Bibliographic record
Abstract
Abstract Some heavy oil reservoirs in Canada show atypically high production rates and high primary oil recoveries under solution-gas drive. Much attention has been given to the anomalous behaviour observed in such heavy oil reservoirs, and several models have been suggested to explain these anomalies. There are two classes of effects responsible for the unusual behaviour of solution-gas drive in heavy oil reservoirs, including fluid effects and rock/geomechanical effects. This study focuses exclusively on fluid effects. In at least two ways, solution-gas drive in heavy oils differs from that in light oils. In heavy oils, the concentration of gas in the oil can be significantly greater than the equilibrium value; the oil could be significantly supersaturated. Additionally in heavy oils, recovery and gas mobility show rate dependent behaviour. Both of these effects are taken into consideration in this study. In this paper, we develop a dynamic model that captures many important processes that affect heavy oil recovery. The non-equilibrium early time behaviour is modelled by introducing a kinetic equation describing the rate of evolution of solution gas into free gas. The equation is derived based on a phenomenological analysis which takes into account bubble nucleation and growth. A second component of this model captures the low gas mobility in heavy oil reservoirs and its dependency on viscous forces. To account for the effect of viscous forces on gas mobility, relative permeability functions are introduced that not only depend on gas saturation but also on local oil phase velocity and viscosity. While many of the previous models apply several kinetic equations associated with a large number of parameters, we have shown that the modifications suggested in this study enable predicting many of the unusual behaviours observed in solution-gas drive in heavy oil reservoirs, using only one kinetic equation with a smaller number of fitting parameters.
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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 | 0.005 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".