Reservoir Oil Characterization for Compositional Simulation of Solvent Injection Processes
Bibliographic record
Abstract
Reliable design of solvent injection for oil recovery requires accurate representation of multiphase behavior in a wide range of pressure–temperature–composition conditions. Volume shift is often required to improve density predictions, separately from compositional behavior predictions, in conventional fluid characterization methods (CM). Thermodynamically, however, volumetric behavior predictions are consequences of compositional behavior predictions. We develop a new fluid characterization method (NM) that gives accurate multiphase behavior representation for oil/solvent mixtures without volume shift. The Peng–Robinson EOS is used with the van der Waals mixing rules. The NM is compared with the CM in terms of phase diagrams, minimum miscibility pressure calculations, and oil displacement simulations. It is shown that the CM with volume shift can give erroneous phase behavior and oil recovery predictions. The NM requires no volume shift to achieve accurate predictions of compositional and volumetric phase behaviors. The two types of phase behaviors are properly coupled in the NM.
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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".