Field Validation: ESP Reliability Monitoring and Production Optimization by Trending Condition and Performance Data Extracted from ESP Surface Electrical Signals
Bibliographic record
Abstract
Abstract This paper presents the results of an ongoing extended field trial on multiple ESP systems in a SAGD field in Canada. The main objective of this field trial is to evaluate the impact of the condition monitoring technology introduced in this paper to address a gap in predictive and preventative maintenance programs for ESP applications. Specifically, the results are used to determine the ability to monitor and quantify the impact of (a) power quality, (b) adverse flow regime and (c) undesirable mechanical vibrations on ESP production and reliability using data extracted from electrical lines supplying the power to ESPs. The data collected from several wells on a single production pad were trended over several months to determine warning thresholds and subsequently operational recommendations to improve overall production and reliability performance. The information collected from this field trial is intended to demonstrate the effectiveness and benefits of this condition monitoring approach in the Operators field. The described condition monitoring technology is based on measurements of high frequency electrical signals, 3-phase current and voltage, from the VFD panel supplying power to the ESP. Previous SPE papers, SPE-201169-MS and SPE-204522-MS, covered the core concept and enabling components of this technology. These papers shows examples on mechanical, electrical and flow regime diagnosis and demonstrated a direct correlation between the periodic mechanical vibration in an ESP and the frequency component in the electrical current signals. This foundational work was largely the basis of the extended field trial discussed in this paper. The results, presented in this paper, demonstrate interesting findings in how this method of condition monitoring can provide meaningful insights into the operation and reliability of the operating ESP systems.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".