Optimizing Downhole Fluid Production of Sucker-Rod Pumps With Variable Motor Speed
Bibliographic record
Abstract
Summary Reciprocating oil pumps are operated traditionally by prime movers rotating at a constant speed. This paper demonstrates that by changing motor speed rapidly within a single stroke, pump production can be increased while stresses in the sucker rod and motor energy consumption are reduced. The optimal motor-speed profile is determined by representing the motor speed with Fourier series and searching for Fourier coefficients that maximize the production while satisfying the imposed constraints on stresses in the rod and on energy consumption. The pump-performance parameters required in the optimization process, such as fluid production, stresses in the rod, and motor torque resulting from a given variable motor speed, are calculated by predictive analysis. The analysis is based on a comprehensive dynamic model of the entire pumping system, comprising surface and downhole equipment. During, and beyond, a minimum 6-month field-validation period, the calculated optimal speed profile has been applied to control the movement of more than 20 pumps currently operating in Alberta, Canada. The resulting increased production and lower operating cost confirmed clearly the benefits of implementing a variable speed of the prime mover to improve pump performance. Presented examples demonstrate an increase of up to 133% in production without increasing energy consumption or loads in the system. Results similar to those with beam pumps can be achieved for hydraulically actuated pumps by applying variable flow rate or pressure to induce the calculated optimal polished rod velocity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".