Historical case of Offshore application of Booster ESP in wells with Heavy Oil and High Percentage of H2S and CO2
Bibliographic record
Abstract
Abstract The purpose of this document is to share the development of the application of Booster ESP in wells with high power requirements and high rates which are located in Marine Region of México where fails means high impacts in costs. These reservoirs are characterized for its high annual decline rate of reservoir pressure, from 4 to 7 kg/cm2 per year what means low statics level of fluid, 2000 tvd, and low pump intake pressures compared to the bubble pressure of the field, in addition to heavy crude oil (10 °API) with low percentage of water (0 – 30%), high percentage (>20%mol) of hydrogen sulfide acid (H2S), high percentage (>10%mol) of carbon dioxide (CO2) and high temperatures (from 120 to 130 °C). The application of this kind of completions or configurations where Dual ESP Pods operate simultaneously, will allow to widen the operational range of those wells or ESP that are limited by power, depth, pressure, or temperature through a distribution of loads between both ESP reducing power requirements and motor temperature and hence, scale precipitation, increasing the ESP run life. Once Booster system design was defined, an application plan which consist of two phases was deployed, the first phase showed that Upper ESP operate with high motor temperature vs Lower ESP, about 4 and 8%, this due to heat transfer. The second phase, where the dual ESP system was optimized and designed according to the well needs show excellent results, reducing current consumption and power requirements between 20 and 40%, furthermore, motor temperature reduce almost 10%. The novelty of this project is in the ability to increase the experience with this kind of operational philosophy with heavy oil, high temperature and low-pressure reservoirs, that will allow to increase run life and reduce operating costs for interventions due to recurrent failures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".