Failure Analysis of the Tripping Operation and its Impact on Well Control
Bibliographic record
Abstract
As the cost of drilling and completion of offshore well is soaring, efforts are required for better well planning. Safety is to be given the highest priority over all other aspects of well planning. Among different element of drilling, well control is one of the most critical components for the safety of the operation, employees and the environment. Primary well control is ensured by keeping the hydrostatic pressure of the mud above the pore pressure across an open hole section. A loss of well control implies an influx of formation fluid into the wellbore which can culminate to a blowout if uncontrollable. Among the factors that contribute to a blowout are: stuck pipe, casing failure, swabbing, cementing, equipment failure and drilling into other well. Swabbing often occurs during tripping out of an open hole. In this study, investigations of the effects of tripping operation on primary well control are conducted. Failure scenarios of tripping operations in conventional overbalanced drilling and managed pressure drilling are studied using fault tree analysis. These scenarios are subsequently mapped into Bayesian Networks to overcome fault tree modelling limitations such s dependability assessment and common cause failure. The analysis of the BN models identified RCD failure, BHP reduction due to insufficient mud density and lost circulation, DAPC integrated control system, DAPC choke manifold, DAPC back pressure pump, and human error as critical elements in the loss of well control through tripping out operation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".