Sizing, Parametric Investigation and Analysis of Automated Sucker Rod Pump using Beam Pump Simulators
Bibliographic record
Abstract
Reciprocating piston artificial lift systems are widely adopted especially, for onshore wells. Matching the pump mode to well and reservoir conditions reduces the pumping cost and increases the efficiency of production. Parameters influencing the energy requirement of sucker-rod lifted oil wells are investigated in this study, and new insights are provided for the parametric investigation of design variables required for sizing beam-pumped wells. Two (2) artificial lift simulators are integrated for automated sizing of beam-pumped systems. A sucker-rod artificial lift system is optimally sized for a case study oil well, to obtain the minimum API rating of the pumping unit, sustain the target production rate, and determine the corresponding minimum prime mover required to drive the pump sustainably. Compared to using a single simulator for the case study, the integrated approach reduces the damped and polished rod horsepower by 54.9% and 26.5% respectively, for a corresponding decrease in minimum NEMA D motor size by 38.6%. These key performance indicators demonstrate the benefits of simulator integration in automated sizing of beam pumps.
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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 | 0.000 |
| 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".