2016
DOI: 10.1016/j.jngse.2016.06.054
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A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents

Abstract: Abstract:Drilling operations of offshore oil and gas fields are characterized by high reliance on advanced technology, high risk, and high costs due to operating in harsh ocean environments, under complicated geological factors, and extreme operating condition. Lost circulation or well "kick" are examples of hazardous events that may occur while drilling wells and such events may develop into a blowout accident if not handled. Identification and analysis of root causes and consequences are effective measures t… Show more

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Cited by 71 publications
(20 citation statements)
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“…The posterior probability of each risk factors was calculated using P ( parent nodes | Pipeline condition ) indicating the probability of parent nodes conditioned to the Pipeline condition . The suspected variables can then be identified by means of posterior probability distribution …”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…The posterior probability of each risk factors was calculated using P ( parent nodes | Pipeline condition ) indicating the probability of parent nodes conditioned to the Pipeline condition . The suspected variables can then be identified by means of posterior probability distribution …”
Section: Methodsmentioning
confidence: 99%
“…Bayesian network approach has also gained a lot of attention in subsea oil and gas domain specifically for risk assessment. There are several adaptations of Bayesian network modeling in risk analysis which show that Bayesian network has demonstrated its capabilities and efficiencies as a practical engineering and problem‐solving tool . The adaptation of Bayesian network approaches in safety and risk assessment is due to several features offered by Bayesian network.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…The BN model was developed from a dynamic fault tree, where Boolean states (failure and success) were used to indicate the probability of failure of specific process equipment [13]. Later on, Wu et al (2016) developed a Dynamic Bayesian Network Model (DBN) based on a Bowtie model for predicting the change of the probability potentially hazardous scenarios with time. Boolean states (Yes and No) were used for represent if and event or equipment failure occurs [14].…”
Section: Figure 2 Bayesian Networkdirected Acyclic Graphmentioning
confidence: 99%
“…Tosida et al (2020a) has implemented human resource intelligence business for telematics businesses in Indonesia based on the Balanced Scorecard on customer and internal business aspects through clustering and pipeline and datalake approaches. As for Wu et al, (2016) who implemented the Deep Belief Network (DBN) algorithm using a limited Boltzman engine compared to the Multilayer Perceptron (MLP) algorithm and the Support Vector Machine (SVM) algorithm. The results show that the flexibility of a deep learning model can provide strong support for fully-informed credit score measurement and a very complex credit risk assessment.…”
Section: Introductionmentioning
confidence: 99%