Investigation of mechanisms involved in generation of foamy oil flow
Bibliographic record
Abstract
Some heavy oil reservoirs in Western Canada and Venezuela under solution-gasdrive show anomalous primary performance: high oil recovery and low production GOR. This anomalous oil production behaviour under solution gas drive has been observed since the 1980's and one of the factors responsible for such behaviour is thought to be foamy oil flow, i.e. flow of gas in the form of dispersed gas bubbles. However, the mechanisms behind the formation of gas dispersion under foamy oil flow conditions remain unclear. There are two contradictory theories to explain the formation of a dispersion: explosive nucleation theory and dispersion due to dynamic equilibrium between the processes of break-up and coalescence. The objective of this work was to further examine the basic mechanisms behind the formation of dispersed gas bubbles and infer which one of the theories of foamy oil flow is consistent with the experiments. The study included a series of sand pack and fluid property measurements, and three series of depletion experiments. Based on the results from these depletion tests, it was concluded that the hypothesis of "explosive nucleation" may not be correct. The mechanism involved in the formation of gas dispersion under solution gas drive appears to be that of the break-up of mobilized gas ganglia. The bubble size distribution is maintained by a dynamic equilibrium between the processes of break-up and coalescence. Some other notable observations were that the capillary number fluctuation corresponded with the fluctuation of simultaneous gas production rate and that the apparent critical gas saturation was 1 % to 16%, increasing with increasing depletion rate.
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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 | 0.001 | 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 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".