Data-driven Bayesian network model for early kick detection in industrial drilling process-Reference-Cited by-同舟云学术

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Data-driven Bayesian network model for early kick detection in industrial drilling process

Published:2020-06 Issue: Volume:138 Page:130-138
ISSN:0957-5820
Container-title:Process Safety and Environmental Protection
language:en
Short-container-title:Process Safety and Environmental Protection

Author:

Nhat Dinh Minh,Venkatesan Ramachandran,Khan Faisal

Funder

Natural Sciences and Engineering Research Council of Canada

Canada Research Chair (Tier I) Program in Offshore Safety and Risk Engineering

Publisher

Elsevier BV

Subject

Safety, Risk, Reliability and Quality,General Chemical Engineering,Environmental Chemistry,Environmental Engineering

Reference27 articles.

1. https://www.bayesserver.com/docs/ 2020 (Accessed 10 February 2019).

2. SMART kick detection; first step on the well control automation journey;Brakel,2015

3. Robust data-driven modelling approach for real-time final product quality prediction in batch process operation;David;IEEE Trans. Industr. Inform.,2011

4. Data-Driven Design of Fault Diagnosis and Fault-Tolerant Control Systems;Ding,2014

5. Kick detection and influx size estimation during offshore drilling operations using deep learning;Fjetland,2019

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