Fiber-Optic-Enabled Coiled-Tubing Operations on Alaska's North Slope
Bibliographic record
Abstract
Abstract Fill cleanouts, matrix stimulation, cementing, and downhole milling are some common coiled tubing (CT) operations. The difference between the success and failure of these jobs relies heavily on the knowledge and prediction of the behavior of downhole parameters such as temperature and pressure. Another significant percentage of CT operations depends on highly accurate depth control to ensure the intended result of the operation. These depth-critical operations include setting packers, tubing patches, CT-conveyed perforating, and zonal isolation. And in virtually all CT operations, including fill cleanouts, stimulations, and cementing, the knowledge of an actual tied-in depth is advantageous to operations. The purpose of this paper is to present a simple and reliable system that allows real-time monitoring of downhole pressure and temperature, and provides depth correlation using a casing collar locator (CCL). In this system, the downhole parameters are recorded in real time without the limitations of conventional wireline-enabled coiled tubing units. The information presented in this paper summarizes the operations performed during the field-testing of the system at Alaska's North Slope. Twenty-seven CT operations were successfully performed using the system; this demonstrated its reliability and provided the crew with the information needed to improve the efficiency of the operations being performed. The system comprises three main components: Downhole tools capable of measuring and transmitting bottomhole pressure and temperature as well as identifying casing collars with high accuracy.Fiber optic and fiber optic carrier, which provides real-time transmission of downhole data to surface.Wireless bulkhead and surface interface modules to provide two-way communication between the tools and the monitoring system.
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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".