Experimental and Modeling Study of Vapor-Liquid Equilibrium for Propane-Heavy Crude Systems at High Temperature Conditions
Bibliographic record
Abstract
Abstract Heavy oil and extra heavy oil reserves have attracted increasing attention in recent years as alternatives to conventional oil reservoirs. Different technologies, such as vapor extraction and steam-assisted gravity drainage, have been developed for in situ extraction of these resources. It was recently found that the compounded effects of solvents and heat on the viscosity of heavy oil can provide oil production rates that may be equivalent to or higher than those from the injection of steam alone. In addition, this improved recovery product is complemented by a process that uses less fresh water and has lower greenhouse gas emissions. There is, however, a distinct lack of basic data and mechanistic knowledge relevant to the solvent/heat-assisted recovery processes. Even the most fundamental experimental data, such as the solubility of commonly used solvents in oil, are not available. The quantitative effects of solvent on oil viscosity and phase behaviors at temperatures approaching the conditions of the in situ steam processes are also not well understood. In the present study, the solubility of propane in an extra heavy oil and the saturated phase densities and viscosities were measured for temperatures from 100°C to 200°C at different pressures. These temperatures and pressures approach the conditions of the in situ steam processes in which both solvent and steam contribute to a higher reduction of oil viscosity. The experimental results showed that, at high temperatures, the saturated phase viscosities converged as the pressure increased. In addition, the density data demonstrated crossover at an intermediate pressure for different temperatures. The data on the solubility and the physical properties can be utilized for the optimization of solvent/heat-assisted recovery processes. The observed phase behavior was modeled using an equation of state model, closely matching the experimental results. Such a model will be useful in the development of solvent/heat-assisted-recovery processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".