Digital Slickline Using a Novel RF Transceiver: Case Studies in Kuparuk River: North Slope, Alaska
Bibliographic record
Abstract
Abstract The Kuparuk River unit (KRU) on the North Slope of Alaska is a maturing asset providing a variety of well intervention opportunities necessary to maintain production. Because of the high well count, interventions need to be efficient, and the traditional slickline and electrical line model are being challenged. The primary concern is the multiple rig ups and rig downs to complete the scope of work, but there are also local concerns, such as maintaining a workable equipment schedule in a cold-weather region. Another unique feature of the KRU is that many of the wells have scale deposits. Digital slickline (DSL) has been successfully used in the KRU and was highlighted in previous papers (Wiese 2015). The ability to have real-time depth correlation with a casing collar locator (CCL) and optional gamma ray (GR) during slickline runs and completing the traditional electric line services (i.e., packer set, perforating, etc.) is a game changer that dramatically helps improve intervention efficiency. A previous challenge was maintaining real-time communications in areas where there is excess scale buildup. To circumvent this issue, a new protocol was developed using a radio frequency (RF) antenna to provide half duplex communications with a coated slickline. This methodology doesnot require the tool housing to contact the tubular to complete the signal transmission. In 2017, more than 400 digital subscriber line (DSL) runs covering a wide variety of tasks were successfully completed, including removing and replacing gas-lift valves, fishing packers, string shots, perforating, setting packers, and patches. An interesting result of the KRU digital slickline interventions was that approximately 60% of the runs were slickline centric involving jars and 40% were considered e-line replacement services. This trend suggests that a successful product should be able to complete all typical slickline runs to maintain the efficiency advantage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".