Experimental Analysis of the Effects of Varying Temperature and Viscosities on Foamy Oil Production
Bibliographic record
Abstract
Abstract The phenomenon of higher than expected production rates and recovery factors in heavy oil reservoirs captured the term "foamy oil," by researchers. This is mainly due to the bubble filled chocolate mousse appearance found at wellheads where this phenomenon occurs. Foamy oil flow is barely understood up to this day. Understanding why this unusual occurrence exists can aid in the transfer of principles to low recovery heavy oil reservoirs globally. This study focused mainly on how varying the viscosity and temperature via pressure depletion lab tests affected the performance of foamy oil production. Six different lab-scaled experiments were conducted, four with varying temperatures and two with varying viscosities. All experiments were conducted using lab-scaled sand pack pressure depletion tests with the same initial gas oil ratio (GOR). The first series of experiments with varying temperatures showed that the oil recovery was inversely proportional to elevated temperatures, however there was a directly proportional relationship between gas recovery and elevation in temperature. A unique observation was also made, during late-stage production, foamy oil recovery reappeared with temperatures in the 45-55°C range. With respect to the viscosities, a non-linear relationship existed, however there was an optimal region in which the live-oil viscosity and foamy oil production seem to be harmonious.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".