Characterization of Foamy Oil and Gas-Oil Flow for Heavy Oil/Propane System in Pressure Depletion Tests
Bibliographic record
Abstract
This work presents a non-equilibrium kinetic model to characterize foamy oil and gas/oil two-phase flow in heavy oil and propane system from pressure depletion tests.Good agreement between experiments data and simulation results are obtained in terms of production data as well as pressure distribution.The following parameters are tuned in the history match process, including k values, gas-liquid relative permeability curves, and reaction frequency factors.The simulation results suggest that bubbles pass through pore throat smoothly and have low dissolve rate in oil phase at low pressure drop rate, which results in high gas recovery factor and low oil recovery factor.Gas bubbles expand to a larger size and block the pore throat when increasing pressure drop rate to intermediate pressure depletion rate.At this range of pressure drop rate, foamy oil and gas/oil flow characterization is influenced by both gas bubbles evolve and dissolve process, which results in low gas recovery and high oil recovery.Continue to increase the pressure drop rate could cause gas bubbles to evolve faster than dissolve back and shorten production period, which results in a relatively low gas recovery as well as low oil recovery.The simulation work presented in this paper successfully characterized foamy oil behavior in the porous media for heavy oil/propane system.The innovative methodology presented in this work could be used as a general method to characterize foamy oil flow in heavy oil/propane system.
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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".