Sealing Casing Leaks through the Remote Deployment of Discrete Particles
Bibliographic record
Abstract
Abstract An outer casing leak can be a significant well integrity issue, primarily due to the inability to easily access the leak site for intervention. Recent outer casing failures caused by external corrosion on some wells in the Kuparuk Field of Alaska prompted research for a non-invasive repair method to delay or negate the need for a rig workover. Limited options for downhole access on outer concentric casing strings have an impact on the ability to define the leak in terms of location, size and shape, and consequently the ability to effect a seal of the casing. The Platelet® technology discussed in this paper is an innovative means of sealing leaks which involves the remote injection of discrete particles into a well which are then carried to the leak site in the fluid flow. When the platelets reach the vicinity of the leak, fluid forces entrain them into the leak and hold them against the casing wall thus facilitating a seal allowing the well to be returned to service. Previously proven for applications in subsea pipelines, platelet technology over the last 12 months has successfully been developed for downhole use. In January 2009, specifically engineered platelets were deployed into well annuli for the first time. Two case studies will be presented where the technology has been used in the Kuparuk field in Alaska. In the first instance a ¼” corrosion induced hole in the surface casing at a depth of 126 ft was sealed with a single platelet by a deployment from surface into the outer annulus. The well was initially leaking at a rate of 0.5 bpm and subsequently passed an 1800 psi mechanical integrity test. The second study will review a seal of a 0.26 bpm leak at 30 ft depth which eventually gave out. The paper will review the front end engineering development, the well site deployment, and lessons learned. The paper concludes by demonstrating the successful use of the platelet technology for sealing annulus leaks and how the technology has successfully postponed or negated the need for a rig workover to repair a surface casing leak. The results from this study have provided valuable insight into the behavior of the platelets in the fluid flow before entrainment and the behavior in the leak after entrainment.
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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".