Integration of PC-SAFT Equation of State into Heat Assisted Solvent Recovery Simulation
Bibliographic record
Abstract
Abstract The accuracy of Equation of State (EoS) in phase behaviour modeling plays an important role in compositional and thermal reservoir simulations. PC-SAFT (Perturbed-Chain Statistical Associating Fluid Theory) EoS can predict solubility and density of bitumen/solvent systems more accurately than Peng-Robinson and Soave-Redlich-Kwong EoS. In this work, a simplified version of PC-SAFT EoS was integrated into a thermal reservoir simulation to model heat assisted solvent recovery process. Bitumen was characterized into two scenarios for the simplified PC-SAFT modeling: eight pseudo-components (8-PCs) and three pseudo-components (3-PCs) using a well-developed bitumen characterization method. The solubility and density modeling ability of 3-PCs was evaluated and verified with that of the 8-PCs system. The simplified PC-SAFT with 3-PCs bitumen characterization method was then applied to generate data of K-Value, density, compressibility, and thermal expansion coefficient of each pseudo-component. The generated fluid properties were integrated with reservoir simulation to model a warm C3H8 VAPEX experiments at 40, 50 and 60 °C. In the 8-PCs characterization, the overall Average Absolute Relative Deviations (AARDs) for the solubility and density of CH4, C2H6, C3H8 and CO2-saturated bitumen were within 6.6 % and 2.3 % respectively, and the predicted AARDs for the density of n-C5H12, n-C10H22, n-C14H30, toluene and xylene-diluted bitumen were as low as 0.9 %. Results of the 3-PCs characterization were found to be as accurate as those of 8-PCs in modeling bitumen/gas solvent systems. In addition, the results of the fluid properties were successfully integrated into commercial reservoir simulator. Relative permeability was history matched with the measured data at 40 °C, and then tested against the measured data at 50 and 60 °C. The reservoir simulation results indicated good agreement with the measured data. The numerical results showed that although bitumen viscosity decreased due to high temperature solvent, solubility was reduced at the same time, compensating the viscosity reduction effects by solvent diluting.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".