Six Zone Intelligent Completion Installation Benefits and Lessons Learned Before Production in Offshore Indonesia
Bibliographic record
Abstract
Abstract Since the late 1990’s, the intelligent completion market has been struggling with the challenge of increasing the number of segmented zonal isolations by minimizing risk and consequently increasing overall production. Risk has been manipulated by restricting the number of zonal segmentations which results in loss of potential zones, non-optimized reservoir drainage due to commingling two or more zones into a single zone impacting reservoir performance. The obvious benefits of intelligent completions are often not visualized until after deployment; the active management of the reservoir with the aided faculty to monitor reservoir performance through down hole gauges and surface digital infrastructure. The often overlooked benefits of intelligent completions are the ones that are invaluable during the installation providing seamless operational excellence with the manipulation of the interval control valves (ICV) for the purpose of testing, and to control potential well issues such as fluid loss and well control. This paper presents a case history from Indonesia in which a six zone intelligent completion was installed inside a single trip multi-zone (six zones) sand control system with complete zonal isolation, control and the ability to measure a total of twelve (12) pressure and temperature points from the subsea well. This document will highlight the challenges involved in bringing a custom solution to reality in a short time from concept to installation. At the same time it will also demonstrate the benefits brought to the operator when managing fluid loss and maintaining well control through the use of the down hole intelligent completion interval control valves prior to landing the completion. Deployment challenges, solutions, successes experienced at the well site and the actual performance of the operations will be detailed in the paper.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".