Using Wireline Standoffs (WLSOs) To Mitigate Cable Sticking
Bibliographic record
Abstract
Abstract The recent development of wireline standoffs (WLSOs) has effectively eliminated cable sticking during deepwater logging operations, offering a viable alternative to pipe-conveyed logging, and saving considerable rig time and risk in the process. On eight high overbalance wells in the Gulf of Mexico, multiple arrays of wireline standoffs (WLSOs) have been successfully deployed to facilitate deep formation sampling without any incidence of cable sticking. This success should be viewed in the context of the fact that the cable force modeling for WLSO deployment indicated that several fishing operations were averted. The oil and gas industry has been using standoffs for years in order to prevent sticking of everything from logging tools to casing. Applying standoff technology to logging cable was a natural progression, although there were many technical challenges that needed to be overcome in order to make the effective use of WLSOs a reality. To begin, the wireline standoffs could not be allowed to damage the logging cable. Yet, these standoffs had to provide a grip sufficient to avoid slippage under high tensions. In addition, the WLSOs needed to be capable of both maximum cable lift and minimal formation contact, while allowing a 3-3/8” fishing grapple to smoothly pass over them. Finally, WLSO modeling had to be able to identify their optimal placement on the cable, taking into account such factors as logging objectives, wellbore trajectory, and applied cable forces (including cable sag between WLSOs in deviated holes). This paper discusses the origins of WLSO design, job planning, and operating procedures. Recommendations for future research into the issue of cable sticking are also included.
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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 | 0.001 | 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".