Experimental Investigation of Foamy Oil Flow Using a High Pressure Etched Glass Micromodel
Bibliographic record
Abstract
Abstract A series of flow visualization experiments were carried out using a high-pressure etched glass micromodel to gain insight into the pore level processes involved in foamy oil flow. The micromodel incorporated a realistic heterogeneous pore network with well-defined pore size distribution and pore throat size distribution. Solution gas drive experiments were conducted using a crude heavy-oil, a deasphalted fraction of the same crude oil, a synthetic mineral oil and a much lighter crude oil. The experimental results show that the rate of pressure drawdown was the most important parameter that altered the flow behaviour in the pore scale level and induced "foaminess" during the solution gas drive process. The dispersed gas flow occurred only in high rate tests and the dispersion was created by break-up of mobilized gas ganglia. Mathematical expressions for nucleation rate were derived for various oil samples. A metering section at the downstream end of the micromodel was used to measure the volume of fluids expelled from the pore network. These volume measurements were used to estimate the total compressibility of the reservoir fluids before the formation of visible bubbles. The compressibility numbers were used to infer the presence or absence of micro-bubbles that would be too small to be seen but could contribute significantly to oil recovery. The estimated compressibility values suggest thatt some microbubbles were perhaps evolved during the depletion process. However, it appears that most of these microbubbles remained attached to the pore walls; only a handful became detached and grew into larger bubbles.
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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".