Developing leading indicators-based decision support algorithms and probabilistic models using Bayesian network to predict kicks while drilling

Predicting a kick timely and efficiently is often a challenging task due to the complexities of drilling and other well intervention activities. This work proposes a leading indicators-based approach to assess drilling operations for predicting kicks and preventing blowouts. A cause-based methodolog...

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Bibliographic Details
Published in:Process safety and environmental protection 2019-01, Vol.121, p.239-246
Main Authors: Tamim, Nafiz, Laboureur, Delphine M., Hasan, A. Rashid, Mannan, M. Sam
Format: Article
Language:English
Subjects:
Algorithms
Barriers
Bayesian analysis
Bayesian network
Blowouts
Control systems
Decision making
Decision support algorithms
Diagnostic systems
Drilling
Drilling kicks
Failure
Indicators
Leading indicators
Mathematical models
Probabilistic models
Probability distribution
Statistical analysis
Well engineering
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Summary:Predicting a kick timely and efficiently is often a challenging task due to the complexities of drilling and other well intervention activities. This work proposes a leading indicators-based approach to assess drilling operations for predicting kicks and preventing blowouts. A cause-based methodology is proposed to develop sets of leading indicators for different categories and organizational levels. Leading indicators are divided into two broad sections – real-time indicators and long-term organizational safety performance indicators. With the real-time indicators, various decision support algorithms are developed which would help to understand a kick progression scenario effectively. Probabilistic barrier failure models for different stages of drilling are also developed to assess performance of primary well control barrier – hydrostatic head. To predict barrier failure events effectively, Bayesian network models are developed combining organizational, operational and real-time indicators. The probability distribution for observing changes in real-time parameters when a kick is in progression are also determined. This study would allow both predictive (causes to effects) and diagnostic (effects to causes) reasoning of kicks and blowouts for better understanding of well control system while drilling. Developed risk models enable informed decision making with a relatively clear picture of the risk of barrier failure and provide useful information on actions required to prevent escalation of well control events.
ISSN:0957-5820
1744-3598
DOI:10.1016/j.psep.2018.10.021