Optimized Milling and Intervention Operations in Low Pressure Wells by Combining Real-Time Downhole Telemetry and Diverting Agents
Bibliographic record
Abstract
Abstract During an underbalanced milling campaign in an area of the Montney formation in the Western Canadian Sedimentary Basin (WCSB) in 2017 to early 2018, a well servicing company experienced a series of three coiled tubing complete immobilization incidents. An initiative was created between the well servicing company and the well operator to address the growing challenges associated with underbalanced milling. The approach was a yearlong process of introducing real time downhole telemetry and fluid loss agents to milling operations. Downhole telemetry was utilized to better understand motor performance, decrease motor damage and identify the key factors in lost circulation events. Far field diverting agents were then pumped through the cleanout or milling bottomhole assembly (BHA) to optimize fluid returns and reduce the required nitrogen volumes. After tracking results, best practices were put in place to ensure a repeatable operation. Not only were immobilization events eliminated, but achievable depth, well complexity and operational efficiencies were all pushed further than predicted. By utilizing data from the downhole telemetry tools, fluid inflow in specific zones was identified as the reason for lost circulation. Best practices were then put in place to identify and rectify fluid inflow in less time than previous practices. Overall time savings were realized by the operator along with repeatable results that reduced financial risk. This paper outlines the technical details that contributed to a new and unique approach to underbalanced coiled tubing interventions that has exceeded the limits previously considered possible in challenging well conditions.
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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 | 0.000 | 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".