Viscosity Modeling of Solvent-Water-Heavy Oil/Bitumen Systems at High Pressures and Elevated Temperatures
Bibliographic record
Abstract
Abstract This work presents a new framework for quantifying the viscosity of a solvent-water-heavy oil/bitumen system as a function of thermal energy, solvent dissolution, and water concentration, respectively. By collecting experimental measurements in a pressure range of 0.9 to 5.0 MPa and a temperature range of 298.2 to 463.3 K, the Peng-Robinson equation of state (PR EOS) together with modified alpha functions respectively for hydrocarbons and water as well as binary interaction parameters (BIPs) has been integrated to quantify the aqueous/liquid/vapor (ALV) and LV phase equilibria. By treating heavy oil/bitumen as either a single pseudocomponent (PC) or multiple PCs, such a framework, along with the volume translation (VT) strategy, effective density, and six mixing rules, successfully reproduces the experimentally measured viscosity from 0.7-566.0 mPa•s with an accuracy of 41.1%, 10.2%, 26.3%, 36.4%, 47.2%, and 47.3% (1 PC) and 30.2%, 9.1%, 19.3%, 35.5%, 40.0%, and 30.1% (4 PCs), respectively. Adding water to a solvent-heavy oil/bitumen mixture can either increase or decrease its viscosity, mainly depending on thermal energy and solvent dissolution. Water concentration in feed plays a crucial role on the mixture viscosity at LV equilibria other than ALV equilibria. Heavier solvents are found to have a superior capacity for diluting heavy oil/bitumen at the same solvent concentration, and water has the same ability for reducing mixture viscosity when it is in liquid phase. At a higher temperature, water as a vapour shows its better ability in diluting heavy oil/bitumen than some solvents (e.g., CO2 and C3H8). Such a newly proposed framework makes it possible to not only dynamically and accurately predict the viscosity for the aforementioned mixtures under various conditions, but also seamlessly integrate it with any reservoir simulators for accurately evaluate and optimize the performance of a hybrid solvent-steam process in a given heavy oil reservoir.
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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 | 0.000 | 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 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".