Parametric Design and Application of Jet Pumping in an Ultra-Deep Heavy Oil Reservoir
Bibliographic record
Abstract
Abstract It is extremely difficult to produce heavy oil from an ultra-deep reservoir due to the long lifting path and the high flowing resistance in the wellbore. During its path to the surface along the production string, the reservoir fluid becomes more viscous resulting from heat loss and evolution of the dissolved gas, and thus movement of reservoir fluid slows down and stops at certain position inside the production string. In this paper, jet pumping has been selected and successfully applied to produce heavy oil from Lungu reservoir, Tarim Oilfield, with a maximum depth of 5950 m. Various power fluids have been examined for their capacities to reduce viscosity of the heavy oil in the production string. Hot water fails to reduce the viscosity of the reservoir fluid due to the significant heat loss along the wellbore, while adding chemicals to water (i.e., activated water) suffers from high material costs. Blending light oil with the reservoir fluid in the wellbore is found to optimally reduce viscosity of the reservoir fluid by more than 1600 times and has been applied in Lungu reservoir. Well configurations for jet pumping technique are designed and analyzed. A theoretical model is formulated to calculate the pressure, temperature, viscosity distributions along the production string, which are subsequently used to determine the key operational parameters, such as the quantity and pressure of the power fluid at the wellhead and the M ratio (ratio of the reservoir fluid to the power fluid). Sensitivity analysis indicates that the viscosity of the light oil and M ratio impose a significant impact on performance of the jet pumping. Field applications show that the jet pumping driven by light oil is a viable and efficient technique to lift heavy oil from the ultra-deep heavy oil reservoir.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".