Real-Time Downhole Monitoring of Electrical Submersible Pumps Rated to 250°C Using Fiber Optic Sensors: Case Study and Data Value in the Leismer SAGD Project
Bibliographic record
Abstract
Abstract Leismer is the first Statoil operated steam-assisted gravity drainage (SAGD) project in the Athabasca region of Alberta, Canada. Electrical submersible pumping systems (ESPs) are the standard artificial lift method for this project. A field trial was planned for newly developed ESPs rated to 250°C. The increased temperature rating allows operating SAGD chambers at higher pressures, thus providing more operational flexibility for increasing recovery and dealing with common exploitation problems. The field trial required reliable and comprehensive down-hole monitoring, so that thorough ESP performance analysis could be performed under real field conditions. Given the extreme conditions at which ESP systems operate in SAGD, fiber optic pressure and temperature sensors were selected for real-time down-hole monitoring. These sensors were placed at the pump intake, inside the motor and at the discharge. The fiber optic gauges’ performance is comparable to standard SAGD measurement devices, but without some of the disadvantages. The sensing system configuration, ESP interface and installation will be described. This paper will also present the value of real-time ESP monitoring. The pump operation is controlled by continuously history matching performance with well performance software and adjusting parameters to changing down-hole conditions. This ensures the ESPs are run near the best efficiency point. Pump intake sub-cool is controlled to minimize steam flashing occurrence. ESP motor temperature is monitored to boost reliability and run time. Finally, discharge pressure measurement has been used for history matching multiphase flow correlations. This improves ESP performance calculation accuracy in the field’s other wells. Integrating ESP advances with fiber optic measurement has allowed effective local technology qualification under real operating conditions. This project has provided abundant information and knowledge for field-wide production optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".