A Pseudobubblepoint Model and Its Simulation for Foamy Oil in Porous Media
Bibliographic record
Abstract
Summary This is the second paper of a series in which we study heavy oil in porous media. The first paper dealt with an experimental study (Wang et al. 2008), whereas a mathematical and simulation study is presented here. The research program stems from the need to predict the field performance of a class of heavy-foamy-oil reservoirs. These reservoirs show a better-than-expected primary performance: lower production gas/oil ratios (GORs), higher-than-expected production rates, and higher oil recovery. A mechanism used to account for the observed performance is that the liberated solution gas is entrained in the oil when the reservoir pressure falls below the thermodynamic equilibrium bubblepoint pressure. The presence of entrained gas increases the effective compressibility of the oil phase and prevents gas from becoming a free phase. Hence, the foamy oil behaves as if it had a pseudobubblepoint pressure below the usual equilibrium bubblepoint pressure. This paper describes a pseudobubblepoint model and a methodology that can be used to compute foamy-oil fluid properties from conventional laboratory pressure/volume/temperature (PVT) data. The techniques developed are then used to study foamy oil in the Orinoco belt, Venezuela. The present mathematical model is validated by comparing numerical and experimental results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".