Recent Achievements Within E-Line Milling
Bibliographic record
Abstract
Abstract Since the concept of milling obstructions on electric line (e-line) was introduced in 2005, operators around the world have applied this technique successfully removing downhole valves, plugs, scales, cement and nipple profiles achieving cost-effective and time-efficient interventions. Recently, a series of e-line milling operations were performed to remove repeater-sub and ball-seat restrictions in oil producing horizontal wellbores located in Southeast Saskatchewan. The low pressure reservoirs favored intervention technologies that did not require excessive hydrostatic head. Operators have traditionally used nitrogen mixed with water to prevent damage to the reservoir and to maintain circulation; however, this reduces the amount of torque that can be achieved at the bit, and causes stalling and sticking issues. Using a combination of tractor and milling technology on e-line in these wells provided the required torque for milling with a steady and constant weight on bit throughout the wellbore for removal of ball seat restrictions. This paper presents the latest achievements within e-line milling in Canada. The paper will discuss best practices of date as well as a discussion of e-line milling challenges through three case studies in Canada.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".