Modeling The Onset Of Asphaltene Precipitation In Solvent-Diluted Bitumens Using Cubic-Plus-Association Equation Of State
Bibliographic record
Abstract
Abstract The growing market for solvent-assisted recovery methods, such as ES-SAGD, SAP, and LASER, increases the risk of asphaltene precipitation in the reservoir and upstream facilities that would result in production loss and huge operational costs. A comprehensive study on the asphaltene phase behaviour and precipitation is necessary in order to design preventive remedies for operational problems arising from asphaltene deposition. In the present study, we employed a cubic-plus-association equation of state (CPA-EOS) to model asphaltene precipitation from Alberta bitumen samples upon addition of n-alkanes. The focus of this work is to study the effect of temperature and solvent on the asphaltene precipitation. This method is, in particular, beneficial to commercial reservoir simulation due to its strong theoretical basis, reasonable calculation complexity, and modest computational time. The physical interactions were described by the Soave-Redlich-Kwong equation of state (SRK EOS). In the association term, the interactions between the bitumen pseudo-components were described based on the Wertheim's perturbation theory. Bitumen was characterized, based on SARA analysis, into two pseudo-components, namely the maltenes and asphaltenes. Self-association between asphaltene molecules and cross-associations between the hypothetical asphaltene and maltene molecules were considered. A solid-liquid equilibrium was assumed at the asphaltene precipitation onset. In order to satisfy the criteria for the formation of a solid phase, we employed a solid solubility model. The proposed CPA-EOS model has only one adjustable parameter, which decreased a lot of unnecessary computational time. This is especially important for systems containing complex materials such as bitumens and heavy oils. The adjustable parameter of the CPA-EOS model was found to be both temperature- and solvent-dependent such that it decreased with increasing temperature and increased with increasing solvent carbon number. Pressure did not have a significant effect on the model parameters for the dead bitumen samples and operational pressures well above the solvent's bubble point. The model predictions were compared with the experimental onset data for asphaltene precipitation over a range of temperature, and they were favourably in agreement. Our results showed that a tuned CPA-SRK EOS combined with the solid solubility model can successfully predict the onset of asphaltene precipitation. The results of the proposed model can be applied to the design and simulation of solvent-assisted thermal recovery methods and processes wherever bitumen comes in contact with heavy n-alkane solvents.
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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".