Reduction of Light Oil Usage as Power Fluid for Jet Pumping in Deep Heavy Oil Reservoirs
Bibliographic record
Abstract
Abstract Jet pumping has been considered as an efficient artificial lifting technique for deep oil production due to its simplicity and lack of moving parts. If light oil is used as power fluid, jet pumping becomes one of the preferred artificial lift methods for the deep-heavy-oil reservoirs by dramatically reducing both viscosity of the reservoir fluid and the pressure loss along the production string. In practice, the amount of light oil required as the power fluid can be as high as three times of the reservoir fluid, among which only a small portion of the light oil is actually needed for viscosity reduction. In this paper, a novel technique has been developed and successfully applied to significantly reduce the amount of light oil usage in a deep-heavy-oil reservoir. More specifically, two approaches are developed and compared. As for Approach A, the oil well is produced with jet pumping driven by light oil first, and then the produced fluid is reinjected into the well as the power fluid. This process keeps circulating until viscosity of the produced fluid is too high to be utilized for jet pumping. As for Approach B, partial produced fluid is combined with the light oil at any reasonable ratios, and subsequently the produced fluid-light oil mixture is reinjected into the well as the power fluid. In the latter approach, viscosity of the mixture keeps increasing and will reach its equilibrium value in a few days, and thus, stable production will be achieved as well. Theoretic models are developed to determine the viscosity of the power fluid for each circulation and the maximum cycle number for the former approach as well as the equilibrium viscosity of the mixed power fluid and the optimum ratio of light oil to the produced fluid-light oil mixture for the latter approach. Field applications show that the reservoir fluid produced from deep heavy oil wells is increased by three times and that the amount of light oil can be reduced by more than 60% for either approach.
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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 | 0.001 |
| 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".