Modeling Phase Behaviour of Solvents/Water/Heavy Oil Systems Under Reservoir Conditions with the PC-SAFT Equation of State
Bibliographic record
Abstract
Abstract As for conventional cubic equations of state (CEOSs), they find their limits in precisely predicting phase behaviour in systems featuring a broad spectrum of molecular sizes, especially liquid densities. Based on the perturbation theory, the statistical associating fluid theory (SAFT) has been used as a standard approach to describe how complex fluids and fluid mixtures with a substantial disparity in molecular sizes behave; however, its application is still limited in heavy oil-associated mixtures due to fundamental and technical challenges. In this work, a perturbed-chain (PC) SAFT equation of state (EOS) has been developed to characterize heavy oil-associated systems containing polar components (e.g., dimethyl ether (DME) and water) and non-polar components (e.g., CO2 and N2) with respect to their phase behaviour and physical properties. Experimentally, constant composition expansion (CCE) tests were meticulously conducted to measure saturation pressure (Psat), phase volume, and phase compositions for CO2/heavy oil, N2/heavy oil, and DME/heavy oil systems in the absence and presence of water, spanning a range of pressure up to 20 MPa and temperature up to 433.2 K. Theoretically, a PC-SAFT EOS model together with temperature-independent binary interaction parameters (BIPs) is integrated to reproduce the measured Psat and other physical properties of the aforementioned systems. It is experimentally found that addition of water into each of the aforementioned systems will increase its Psat compared to that without water. By characterizing heavy oils as four pseudocomponents (PCs), the density (ρ) of the aforementioned systems can be accurately predicted with the root-mean-squared relative error (RMSRE) of 1.84%. Then, the BIPs for each binary pair of the aforementioned systems are obtained by minimizing the discrepancy between the measured Psat and the calculated ones. The proposed model shows its superior performance over the widely used CEOS (i.e., Peng-Robinson EOS) with an RMSRE of 2.93% for the predicted Psat of the aforementioned systems. The theoretical model proposed in this study excels to reproduce the experimentally measured phase behaviour and physical properties under reservoir conditions, allowing us to accurately evaluate and optimize the hybrid steam-solvent processes in a heavy oil reservoir.
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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".