Transformational Reduction in Non-productive Time through Implementation of Broadband Networked Drill-string and Modified Drilling Practices in an Extended Reach Drilling Campaign
Bibliographic record
Abstract
Abstract Historically, extended reach offshore wells in Trinidad & Tobago were characterized by wellbore instability and hole cleaning challenges. These phenomena had become almost impossible to manage effectively in the highest angle wellbores, leading to levels of non-productive time (NPT) that threatened the economic viability of the latest development. Wellbore instability exacerbated the hole cleaning challenge, fueled by a new instability mechanism at highest wellbore inclinations. This led to pack-offs and stuck pipe incidents. Additionally, poorly understood and generally insufficient hole cleaning practices increased stuck pipe risk, and also caused the equivalent circulating density (ECD) to rise, resulting in mud losses due to the narrow window between mud density required for wellbore stability, and formation fracture gradient. The solution to these problems was found through advanced downhole measurements of borehole stability and hole cleaning, transmission of those data back to surface via a high frequency medium ("networked or wired" drill-pipe), deployment of subject matter experts into the rig team for critical phases of the operation, and introduction of unconventional drilling and decision-making practices to mitigate the problem phenomena. This paper describes the transformational efficiency improvement that was achieved by this combination of new technology, improved workflows, and multidisciplinary expertise deployed to the rigsite. The methodology was implemented on the third well, resulting in a reduction of NPT from 47% and 48% on the first two wells, to 10% on the third, clearly expressing the enhanced control of these drilling phenomena on the third well. Recommendations offered are relevant to many extended reach drilling campaigns, and may be critical to the success of ultra-ERD wells.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".