Front End Maturation of Thermal Electrical Submersible Pump Design in a Steam Development in the North Oman
Bibliographic record
Abstract
Abstract Habur Project will be the second fractured carbonate reservoir using Thermally Assisted Gas Oil Gravity Drainage (TAGOGD) recovery mechanism in Northern Oman. ESPs are selected to provide artificial lift ahead of gas lift to reduce Capex, lower power consumption and to increase well lifting potential. This paper summarizes the approach used to mitigate risks in the deployment of thermal ESPs in potentially extreme corrosive (high H2S and CO2) operating conditions. The ESP operating environment is predicted to be challenging due to a unique combination of conditions: i) high temperature (> 150degC), ii) high free gas/steam production, iii) corrosive with high H2S/CO2. These challenges pose a risk that the pumps are not able to achieve required functionality and/or have a short lifespan. Front-end integrated mitigation plan of the above has been constructed based on i) internal and external ESP performance benchmarking, ii) early detailed "bespoke" thermal ESP design options, iii) initial operating procedures and iv) troubleshooting guidance. Additionally, the project economic robustness was tested against different ESP run life scenarios. Thermal ESPs are a mature artificial lift method with local and global deployment in hot environments such as Steam Assisted Gravity Drainage (SAGD) systems. If steam breakthrough is not appropriately managed, it can result in low pump efficiency or gas lock in some cases which has a negative impact on production. Experience from SAGD in Canada indicates that with proper ESP design and appropriate trouble shooting procedures, risks related to gas and steam breakthrough can be managed. Corrosion can lead to premature ESP failure. Habur Project is predicted to become sour as the rock and reservoir fluids heat up releasing significant CO2 and H2S. However, the risk of corrosion can be mitigated by appropriate material selection of ESP components. Expensive metallurgy raises the cost of pump deployment, but economic analysis shows the project to be robust even in the scenario of short ESP lifespans. This project demonstrates how through benchmarking, multidiscipline integration and early collaboration with ESP service providers supported maturation of a robust ESP design to support project Final Investment Decision. Importantly this work expands the operating conditions of thermal ESPs beyond typical clastic SAGD such as Canada to more corrosive environments expected in carbonate steam developments. Novel workflows described in this paper can be adapted to other challenging high temperature and corrosive fields using TAGOGD or other thermal recovery methods.
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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 | 0.000 |
| 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".