Operator/Service Company Research Collaboration Leads to Reduced Downhole Vibration Levels and Improved Drilling Performance
Bibliographic record
Abstract
Abstract The Cana Woodford shale play is emerging as an active natural gas field in Oklahoma, USA with over 100 wells already drilled and many more in the pipeline. However, the key to success, especially with the current depressed pricing of natural gas, is continued injection of technology leading to reduced drilling costs. The most common well profile drilled in Canadian County and Blaine County, Oklahoma consists of a vertical section followed by a curve section and a long horizontal section through the Cana Woodford shale. Downhole vibrations were identified as major drilling performance limiters in the 8 ¾ in. and 12 ¼ in. vertical hole sections in these applications. The operator and the service company started a collaborative project to measure, understand, and reduce vibrations of the bit and BHA in order to improve drilling performance. The project adopted a holistic approach that included the use of analytical drilling models, drilling lab facilities, an experimental test rig and field testing to study how bits, BHA's and operating parameters impact downhole vibrations. All testing in the drilling lab, at the experimental test rig, and in the field incorporated in-bit vibration sensors. In this paper, the authors will illustrate how a collaborative effort between an operator and a service company can leverage the collective knowledge and experience of both organizations for scientific investigation into the most important drilling dysfunction -downhole vibration. Based on testing in the lab, the experimental rig and the field, changes in the BHA and drive mechanism were implemented. These changes led to significant reductions in vibration levels, improved performance and better bit dull conditions. It is anticipated that as the collaboration moves forward, reductions in downhole vibration will translate into continued improvement in ROP, longer tool life, and significant cost savings for the operator.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".