Going Long – Overcoming Challenges in Completing 3600m Laterals
Bibliographic record
Abstract
Abstract Recent trends in unconventional oil and gas developments have seen longer horizontal wells drilled to achieve greater reservoir contact while minimizing cost and surface impact. Challenges for completing longer laterals include achieving effective fracture stimulation and performing clean out of the well bore after stimulation is complete. A case study was performed in Shell Groundbirch, an unconventional gas development in British Colombia, Canada, focusing on stimulation and extended reach cleanouts. Five long lateral wells +3600m lateral length with measured depth to true vertical depth ratio of 2.5, were drilled and completed; the resulting wells are 60% longer than the standard development well. Based on operational efficiency, coiled tubing (CT) was determined to be the preferred method for performing the millouts and well cleanup. 73.0 mm CT with an aggressive taper can reach the required set-down depths with enough weight on bit available to mill-out all completion plugs. Operational plans and milling tools were developed to maximize the probability of success. This paper outlines the technical details which contribute to an important case study that can help define and push the limits of extended reach CT interventions in industry.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".