Openhole Completion Options: The Niger Delta Experience
Bibliographic record
Abstract
Abstract Within the Niger Delta clastic environment, horizontal well completions have been widely used with success. Although, conventional wells have been applied to drain reservoirs in Niger-Delta extensively, in recent years, horizontal wells have also gained acceptance as a proven reservoir management and well completion method. Production improvement factors (compared to conventional wells) of two or higher is not uncommon. To make decisions on the correct completion type to select, it is important to be aware of the many sand control issues and the relative strengths/weaknesses of the systems available. Production hotspots arising from partially plugged screens are often a problem, giving rise to the challenge of installing rugged sand face completions, which again could also compromise production. When it comes to selecting a sand face completion strategy, several operators have a number of concerns. This paper examines sand control options (barefoot, standalone predrilled liner / screens or slotted liners, gravel pack and expandables) for the Niger-Delta, a high activity region in openhole applications. Typical key learning is presented. The openhole completions are preffered over casedhole completions in the relatively high transmissibility reservoirs of the Niger Delta as they will allow higher well productivity to be attained and their greater inflow area is also advantageous in solutions where scaling can occur like in water injection for pressure maintenance. Wells drilled and completed with a fully integrated study were profitable, emphasing that planning drilling and completion as a discrete system, performing drill-in-fluids QA/QC, ensuring a stable hole, and that all engineers take active roles to make proactive decisions / choices that can impact operations are keys to success for a typical completion method. Small independents that operate marginal oil wells will find openhole completion on horizontal wells useful to enhance production rates. Even the majors will agree that prevention based on experience and sound engineering practices is better than cure for most sand control problems. A common score card item, irrespective of the category of well owner is to deliver best-in class producers that quickly pay back investment capital.
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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 | 0.003 | 0.004 |
| 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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".