Advanced Numerical Simulation of Solvent Vapour Extraction (SVX) Processes
Bibliographic record
Abstract
Abstract This work presents advanced numerical simulation techniques that were developed to improve the history matching and predictive capability of both laboratory and field scale SVX processes. An algorithm was developed to incorporate the time dependence of SVX processes in the CMG STARS software; it incorporated a non-equilibrium solvent solubility method. A new methodology was developed to correct for excessive numerical dispersion effects at field scale gridblock sizes. Thermal effects due to solvent dissolution in heavy oil were also studied. The advanced numerical simulation technique was constrained by PVT mixing experiments designed to generate mass transfer and non-equilibrium solvent solubility datasets for the solvent–oil system. The numerical models were tuned from the datasets obtained from three lab scale 3D SVX physical model experiments and by the use of a unique set of gas-liquid relative permeability curves (applicable to all three experiments) that employed the solvent mass transfer rate as the history matching parameter. The three physical model experiments varied in only model geometry and horizontal well placement. A relationship between reaction frequency factor (ra) and gridblock size was developed to correct for excessive numerical dispersion effects at field scale gridblock sizes. Thermal effects were incorporated in the numerical model by properly assigning the thermal parameters describing the fluid and porous media heat capacities and vaporization enthalpy coefficients. The studies found that simulations conducted using a non-equilibrium solvent solubility method yielded more realistic relative permeability (kr) curves. The dissolved solvent concentrations and diluted oil viscosity profiles were also more realistic. The viscosity-reducing potential of the thermal effects of solvent dissolution were found to be negligible when compared with the viscosity-reducing effects of solvent dilution.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".