Lessons Learned from an ESP Development of a Naturally Fractured Carbonate Reservoir Offshore Gabon
Bibliographic record
Abstract
Abstract The Ompoyi and Orindi fields are located 5 km offshore Gabon in a water depth of 20 m. The high oil density (23° API) and high produced water salinity (150 kppm) combined with reservoir pressure depletion meant that primary production of the Ozouri reservoir required artificial lift. Initially, progressing cavity pumps (PCPs) were selected, however, following failures of the elastomers, electric submersible pumps (ESPs) were deployed to produce at higher rates with high gas/oil ratios (GORs). The production instability associated with the dual-porosity reservoir behaviour and high free-gas content in both the inflow and outflow of the wells presented the main challenge to ESP design and operation. Additionally, most of the wells were remote from the main production facility and were produced via multiphase subsea flowlines, which made well testing difficult due to the phase segregation in the flowlines. To achieve economically viable production utilizing ESPs, innovative use was made of a range of existing technology. Operationally, however, real-time monitoring was essential to setting wellhead pressures and pump speed to maximize drawdowns. Key technology elements were monopods to minimize the offshore structure cost, fit-for-purpose subsea power cable and helicoaxial downhole pumps for high gas void fraction operation. Key lessons were learned following the trial of several completion architectures to find the optimal combination of gas venting, reservoir access, and a dual barrier mechanism. The first well was put on production in 2002. Since then a further 10 wells have been drilled to reach a total liquid production of 8,000 B/D with a 60% water cut. Production has been economical and thus confirmed ESPs as being the right solution for this reservoir. The lessons learned prove that the application of ESPs is not limited to traditional waterfloods and that it is feasible to produce challenging reservoirs with ESPs.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".