Phase Behavior and Viscosity Measurements of Heavy Crude Oil With Methane and Ethane 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. Over the last two decades, new techniques have been developed to take advantage of solvent and heat in heavy oil recovery. In these methods, referred to as solvent/heat-assisted recovery processes, the compound effects of solvent and heat on the bitumen viscosity may provide bitumen production rates that may be equivalent or even higher than those from the injection of steam or solvent alone. Experimental data on the phase behavior of heavy oil / solvent systems and their physical properties are essential for the design and optimization of such processes. The plain fact is the lack of data at the high-temperature conditions approaching those of in situ steam injection. In the present study, the solubilities, saturated phase densities and viscosities for methane and ethane in extra heavy oil were measured for a temperature range of 50 to 190°C and pressures up to 8 MPa. These temperatures and pressures cover the conditions of the in situ steam processes. The experimental results indicate that the reductions of the saturated viscosity and density with pressure at high-temperature conditions were not as significant as those at low temperatures for both solvents. However, equilibration time was reduced significantly, and the application of these processes in the field has become practical. The saturated viscosity data versus pressure showed a linear trend at high temperatures and a nonlinear reduction at low temperatures. Furthermore, ethane had a higher viscosity reduction than methane at all conditions. This difference was more significant at lower temperatures. The gas solubility data for all cases demonstrated a linear increase with pressure at different temperatures.
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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) | 0.001 | 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".