Digitalizing the Management of Electric Submersible Pump Failures Through Failure Prevention and Post-Mortem Analysis Tools
Bibliographic record
Abstract
Summary The replacement and maintenance of Electric Submersible Pumps (ESP) represents a significant operating expense for any in-situ Oil Sands operation. The goal at Suncor's in-situ operations is to effectively and efficiently manage ESP failures to lower costs, maintain production, reduce carbon emissions, and contribute to safe operations. This goal is in part, accomplished through a collection of software tools that automate and enhance Production Engineering workflows related to the management of over 300 ESPs. An overview of the overall digitalization effort, including information about the development and rationale behind the centralized ESP information and analytics database, various failure prediction tools and anomaly detection methodologies, and the application that delivers these solutions is discussed. The estimated impact of this effort is measured through accuracy. The ideas behind the effort have, both directly and indirectly, contributed to the increase in ESP run life, the decrease in ESP replacement time, the decrease in person-hours per ESP required for management, the increase in safe operations, and the increase to general well availability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".