ESP Gas / Vapour-Locking Detection, Quantification, and Avoidance Using High Frequency Electrical Analysis and Advanced Process Control
Bibliographic record
Abstract
Summary The overall objective of this work is to utilize high frequency ESP condition monitoring data to develop an automated control loop to detect and avoid ESP low-flow interruptions due to gas/vapour interference. This paper shares the results of an ongoing project incorporating high frequency electrical data analysis to the existing ESP control systems on multiple wells / pads in a SAGD field in Northern Alberta, Canada. In our previous paper, SPE-214728-MS, we demonstrated use cases of high frequency electrical analysis of ESP motor for diagnostics of issues related to mechanical, electrical, and adverse flow regime. This new work focuses mainly on how this high-frequency ESP electrical data can be utilized for the early detection of adverse flow regime events such as gas/vapour locking. By utilizing an algorithm developed to detect ESP pump cavitation, a process loop was developed for the advanced process control (APC) systems that dynamically adjust the operating conditions of the ESP to avoid low-flow events while maximizing production. We compare the production conditions for 3 ESPs running with and without the cavitation detection logic. The results will demonstrate how ESP's can operate without low-flow interruptions and with comparable production rates to the optimum rate for these wells. This logic helped to achieve the objectives of optimizing production while avoiding low-flow interruptions with minimum operator engagement. Low-flow events due to gas/vapour locking are challenging in both conventional and unconventional production. This work aims at quantifying these events and enabling the APC to automatically optimize ESP production while avoiding gas/vapour induced ESP low-flow events.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".