Permanent Magnet Motor Large Scale Application in Studied Field of Bohai Bay, China
Bibliographic record
Abstract
Abstract China's emphasis on energy conservation and emission reduction aims to achieve carbon neutrality by 2060. The studied oilfield has around 500 producers and ESP power accounts for about 37% of the total power consumption. This paper presents the implementation and lessons learned from a successful field-wide deployment of Permanent Magnet Motor (PMM) driven ESP/ESPCP systems to reduce field-wide power load and unit costs, by offsetting fuel gas imports and lowering carbon emissions. To address the high energy consumption and low efficiency of ESP systems offshore, specially designed PMM+ESP/ESPCP artificial lifting systems were deployed. The study primarily focuses on PMM+ESP systems while there is only one PMM+ESPCP system currently in operation. Breakthroughs in downhole technology include the development of high-power PMMs, increasing motor power to meet the requirements of larger loads of 200kW and above, and unique electromagnetic design to ensure constant motor speed requirements of centrifugal pumps. The ground technology upgrade includes long-distance cables (3000m and above) transmission to meet the PMM control requirements, compatible with the Voltage/Frequency (V/F) control mode used by existing offshore platforms, and optimize surface equipment control parameter models to design a special safety isolation box to ensure the safety and reliability of the PMM after the pump is stopped, and meet the environmental safety management and application requirements of offshore platforms. Current anti-feed voltage technology proved to be an effective way to prevent feed-voltage risks of PMM. Compared against Induction Motors (IM) power consumption, PMMs have an average energy-saving efficiency of more than 20%. Since February 2023, the studied oilfield has been pilot testing PMM technologies, and in October 2023, the first platform fully lifting with PMMs was put into production, marking the beginning of large-scale application of PMMs in China's offshore oilfields. As of Feb 28th 2025, 80 wells have been installed with PMMs, with the longest running days reaching 758 days, average daily power savings of 650 kWh per well, annual power savings per well of 237 thousand kWh, saving approximately 237,000 yuan (34,000 USD) in electricity costs, reducing annual carbon dioxide emissions by about 192 tons per well, average power saving rate of 20.32%. PMM's deployment significantly improves energy-saving effects and effectively reduces carbon emissions from fuel gas as well as reduces adverse environmental impacts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".