Successful Field Implementation of an Integrity-Focused Digital Monitoring System for Tubular Running in a Challenging Thermal ERD Application
Bibliographic record
Abstract
Abstract Horizontal liners in extended-reach drilling (ERD) wells can experience severe loading during running. Sometimes, downhole loads approach the limits of the tubular system and must be actively managed to ensure long-term well integrity. This paper describes a Canadian thermal operator's approach to managing installation and service performance of slotted liner and wire-wrapped screen systems in a steam-assisted gravity drainage (SAGD) application with unwrapped reach ratios approaching 13:1, and the associated evolution of liner running practices. The Operator's approach combines well-characterized liner body installation loading limits and a rigsite digital solution that leverages available measurements and a real-time torque-and-drag and tubular integrity monitoring system to inform the drilling team during running. Surface loads and rates measured by the rig are used as input to top-down torque-and-drag analysis to estimate downhole load distributions. Those downhole load estimates are then compared to the local loading limits of the liner at all depths. These local loading states (and their associated uncertainties) are integrated into a safe surface loading envelope that is displayed to the drilling team and updated in real time to support running decisions. The evolution of the Operator's running practices has provided a strong basis for confidence in protecting a critical tubular system, and over 250 liner runs have been monitored to date using the digital system. Prior to implementing the system, a conservative approach to managing downhole loads during liner running was used. The integration of a strong engineering basis for the tubular structure with top-down torque-and-drag analysis and uncertainty characterization has provided a running optimization basis and measurable indicators of tubular health that can serve as an enduring quality record and be referenced for the remainder of the well life. Forecasting of running loads and liner limits to total depth has also enabled early recognition of running challenges and opportunities for optimization. Interestingly, the edge-deployed digital system has also led to operational efficiencies during the running process. Running stages involving higher risk to tubular integrity are recognized early and treated with due care, as are opportunities for increasing the efficiency of certain parts of the running process. As the Operator considers longer-reach wells, the system also provides insights into likely running challenges and provides strong history-match datasets that provide a field-calibrated basis for predicting running and tubular integrity limits. The Operator leveraged a novel digital methodology for monitoring liner system integrity during well construction. The ongoing use of this system has allowed optimization of planning, real-time, and post-run practices, and provides a well-conditioned historical dataset for future well planning. The methodology has enabled the Operator to unify work done by drilling engineers, consultants, and the rig crew for optimal liner system integrity and running efficiency.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".