Development of an AMPCP Condition Indicator
Bibliographic record
Abstract
Abstract All-metal progressing cavity pumps (AMPCPs) are frequently used in oil and gas applications to produce high-temperature fluids to surface. Unfortunately, due to the interaction of the metal rotor on the metal stator, AMPCPs can exhibit accelerated wear during operation that results in decreasing pump volumetric efficiency over the pump's life. While decreasing pump volumetric efficiency can be a result of pump wear, it can also be due to changes in downhole operating conditions, such as increasing pump differential pressure or decreasing pump speed. For this reason, when evaluating the state or condition of an AMPCP, the use of pump efficiency alone may be a misleading or imperfect indicator. In this work, a proposed condition indicator is presented that estimates the wear experienced by an AMPCP at any given time in operation and takes into consideration the potential effects of downhole operating conditions. The proposed condition indicator can also be extrapolated into the future and used to estimate the potential remaining useful life (RUL) of an AMPCP by considering the current pump condition and the estimated future downhole operating conditions. An example is provided to demonstrate how to calculate the condition indicator of an AMPCP and several case studies are presented that illustrate the effects of varying downhole operating conditions and various workover activities, such as rotor re-landing and rotor swaps, on the condition of an AMPCP and its potential RUL. The proposed AMPCP condition indicator enables operators to estimate the condition of an AMPCP over time, which allows for better pump selection based on the anticipated operating decisions, better operating practices to maximize system performance, as well as improved planning of upcoming workovers based on more accurate AMPCP RUL estimates.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".