Experimental Measurements and Correlation of K-value, Viscosity, and Density Data for Mixtures of Light to Heavy Solvents and Athabasca Bitumen with Applications of ES-SAGD Process
Bibliographic record
Abstract
Abstract Alberta's oil sands reserve is one of the largest known hydrocarbon deposits. Current commercial recovery methods are steam assisted gravity drainage (SAGD) and cyclic steam stimulation (CSS). These methods need a large amount of steam per unit volume of oil produced. This results in both high energy consumption and enormous impact on environment due to water treatment and greenhouse gas emissions. Expanding-Solvent SAGD (ES-SAGD) has been suggested to overcome the problems associated with these processes by associating solvent with steam injection. ES-SAGD not only reduces the environmental impacts but also increases the bitumen production efficiency. Therefore, ES-SAGD results in higher oil recovery with less production costs and reduced environmental impacts. The key pre-requisites for simulation and design of ES-SAGD process are the phase behaviour data consisting k-value, density, and viscosity of solvent-bitumen mixtures at high temperatures up to 250 °C and moderate pressures up to 6 MPa. In this study, experimental k-value data of carbon dioxide and different hydrocarbon solvents in bitumen as well as experimental measurements of density and viscosity of solvent-bitumen mixtures are presented. Carbon dioxide and hydrocarbon solvents of methane to pentane are covered. The most of ES-SAGD projects are operated at temperatures close to 250 °C while the available experimental data in literature covers the temperature conditions up to 180 °C. Accurate correlations with solid thermodynamic background are presented to estimate the data at high temperature regions required for simulation of ES-SAGD process. These correlations are evaluated with the new measured experimental data. The results showed significant difference between behaviour of light and heavy solvents in low and high temperature regions. At low temperature conditions, viscosity reduction of light solvents is more pronounced than heavy solvents while at high temperature regions heavy solvents are more effective. Comparison between the experimental data at high temperatures (240 °C) and the modelling results showed that the proposed correlations can be applied where the experimental data at high temperatures are not available. The experimental data and correlations in this study can be used for simulation and optimization of ES-SAGD processes in Alberta bitumen reservoirs.
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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".