Circulating Usage of Partial Produced Fluid as Power Fluid for Jet Pump in Deep Heavy-Oil Production
Bibliographic record
Abstract
Summary Jet pumping driven by light oil is one of the preferred lift methods for producing heavy oil in a deep heavy-oil reservoir. Generally, the amount of light oil required is too large to be acceptable. One solution which reduces the amount of light oil required is to blend light oil with a portion of the produced fluid at a reasonable ratio. Then, the produced fluid/light-oil mixture is reinjected into the well as the power fluid. In this case, the viscosity of the blended power fluid keeps increasing and eventually reaches its equilibrium value, which has been found to be a function of reservoir-oil viscosity, light-oil viscosity, the ratio of light oil to blended power fluid (volumetric percentage), and the ratio of well rate to diluent rate (M ratio). Moreover, an optimal ratio of light oil to blended power fluid can be determined by using an iterative algorithm developed in this study. Variations in any of the previously mentioned parameters, especially the viscosity of light oil and the ratio of light oil to blended power fluid, result in a significant change in both the viscosity of the blended power fluid and the pressure loss in the production string. It has been shown in a field application that the amount of light oil used for driving the jet pumping operation can be reduced by more than 50%.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".