Reliability Improvement of Progressive Cavity Pump in A Deep Heavy Oil Reservoir
Bibliographic record
Abstract
Abstract It is extremely difficult to produce heavy oil from a deep reservoir where oil produces together with sand in an unconsolidated formation. The traditional progressive cavity pump has found its limited reliability in such a deep reservoir due to a lower fluid level, which leads to a larger torque and heavier load as both the pump setting depth and forces imposed on the rod string are increased substantially. In this paper, a pragmatic efficient technique has been developed to improve reliability of the progressive cavity pump in a deep heavy oil reservoir where oil produces together with sand in an unconsolidated formation. More specifically, tapered thread has first been used to replace the conventional ones for strengthening the resistive torque of the entire string as the collar is identified as the weakest point from both field applications and force analysis. The rotary anchor equipped with scalable slips is then adopted to not only avoid the tubing bend generally resulting from the compressed setting anchor, but also be easily set at the bottomhole where sand is accumulated. In addition, a frequency converter is used to control the rotating speed, which allows the pump to be started and shut off smoothly. It has been found from over 200 wells that the newly developed technique can be used to greatly improve the reliability of the progressive cavity pump for increasing oil production from deep heavy oil reservoirs by significantly extending the life of the rod string.
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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 | 0.001 | 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".