Phase Behavior Measurements and Modeling for N<sub>2</sub>/CO<sub>2</sub>/Extra Heavy Oil Mixtures at Elevated Temperatures
Bibliographic record
Abstract
Recently, a new technology, the so-called multithermal fluids huff and puff, to exploit extra heavy oil reservoirs has been applied successfully in several shallow heavy oil reservoirs in China. However, the use of this technology in deep extra heavy oil reservoirs is rare. Successful application of this technique in deep extra heavy oil reservoirs requires a good knowledge of the phase behavior and physical properties of multithermal fluids and extra heavy oil mixture. The major components of multithermal fluids mixtures are steam, N 2, and CO 2 . In this work, targeting the application of multithermal fluids injection in heavy oil reservoirs, we conduct PVT experiments on the N 2 /CO 2 /heavy oil mixtures under deep reservoir conditions and develop equation of state models for representing these PVT data. Experimentally, it is found that CO 2 solubility in extra heavy oil decreases with an increasing temperature at given pressure. Contrary to CO 2, N 2 solubility in extra heavy oil increases with an increasing temperature at a given pressure. Theoretically, the extra heavy oil is split and lumped into eight pseudocomponents to characterize the critical temperatures, critical pressures, acentric factors, and other properties by using the Kesler–Lee formulas. To match the solubility obtained in the experiments, two binary interaction parameter (BIP) correlations in the Peng and Robinson equation of state (PR EOS) are selected to calculate the solubility of both N 2 and CO 2 in extra heavy oil (Peng and Robinson, 1976). At a given temperature, the exponent in each BIP correlation is optimized to match the measured solubility of N 2 or CO 2 in extra heavy oil. We validate the optimized BIP exponents in the PR EOS model by using them to reproduce the measured saturation pressures and swelling factors of the ternary N 2 /CO 2 /heavy oil mixtures. The validation results show that the BIP correlations with the optimized exponents can reproduce the measured swelling factors and saturation pressures of N 2 /CO 2 /heavy oil mixtures with good accuracy. In addition, by carrying out example calculations using the tuned PR-EOS model, we discuss the possible multiphase equilibria that can be encountered under reservoir conditions when the multithermal fluids (N 2 /CO 2 /H 2 O) are injected into an extra 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 | 0.001 |
| 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".