Effect of EOS Characterization on Predicted Miscibility Pressure
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Bibliographic record
Abstract
Abstract An equation of state (EOS) is not a unique representation of the PVT behavior against which it is tuned. Two equations of state with different parameters may appear to predict the same experimental PVT data about equally well. However, they may give very different simulated slim-tube recovery as well as analytical predictions of minimum miscibility pressure. In addition to PVT data, tuning the EOS against slim-tube data is advisable. This paper illustrates the above remarks for three different reservoir fluids that have different amounts of PVT data to tune the EOS against. These examples show that what appear to be equally acceptable EOS characterizations with regard to PVT data predictions can predict differences in miscibility pressure as much as 500 to 1000 psia. In an example simulation in a heterogeneous reservoir cross section, these differences in EOS characterization caused 7 to 22% differences in predicted incremental recovery.
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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 it