Phase Behaviour and Physical Properties of Dimethyl Ether (DME)/CO2/N2/Water/Heavy Oil Systems Under Reservoir Conditions
Bibliographic record
Abstract
Abstract The application of a mixture of dimethyl ether (DME) and flue gas is a promising method to recover heavy oil as DME is first-contact miscible with hydrocarbons and partially miscible with water, CO2 can accelerate mass transfer, and N2 can boost the energy in a depleted heavy oil reservoir; however, phase behaviour and physical properties of DME/CO2/N2/water/heavy oil systems are still not well quantified. In this study, theoretical and experimental techniques are developed to determine phase behaviour and physical properties of the aforementioned systems at pressures ranging from 2 MPa to 20 MPa and temperatures spanning from 352.15 K to 433.15 K. In addition to collecting experimental data from the public domain, eight constant composition expansion (CCE) tests are carried out. A thermodynamic model that incorporated the Peng-Robinson equation of state (PR EOS), a modified alpha function, the Péneloux volume-translation strategy, and the Huron-Vidal (HV) mixing rule is used to reproduce the measured phase equilibria data. The tuned binary interaction parameters (BIPs) are utilized in conjunction with the thermodynamic model to accurately predict saturation pressure (Psat) and swelling factor (SFs) with a root-mean-squared relative error (RMSRE) of 3.32% and 0.57%, respectively. Furthermore, the recently proposed model demonstrates its high accuracy in forecasting the oleic/vapor (LV) two-phase boundaries for N2/heavy oil systems and DME/CO2/heavy oil systems with an RMSRE of 1.93% and 2.77%, respectively. Similarly, the accuracies of the predicted aqueous/oleic/vapor (ALV) three-phase boundaries for N2/water/heavy oil systems and DME/CO2/water/heavy oil systems are 2.85% and 3.47%, respectively. Besides, water is found to increase the phase boundaries for DME/CO2/heavy oil systems but decrease those of N2/heavy oil systems and DME/CO2/N2/heavy oil systems. Additionally, as the concentration of N2 and CO2 in the feed mixture is increased, its Psat is increased. In this work, new PVT experiments are conducted to evaluate the impact of adding DME/CO2/N2 into the heavy oil bulk phase in the absence and presence of water. The developed model accurately characterizes the phase boundaries and physical characteristics of the reservoir fluids containing polar components, which are essential for design, evaluation, and optimization of hybrid steam-solvent injection processes in heavy oil reservoirs.
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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".