A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents

Wu, Shengnan; Zhang, Laibin; Zheng, Wang; Liu, Yiliu; Lundteigen, Mary Ann

doi:10.1016/j.jngse.2016.06.054

Cited by 69 publications

(20 citation statements)

References 31 publications

(34 reference 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%

“…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%

“…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. [7][8][9][10][11][12] The adaptation of Bayesian network approaches in safety and risk assessment is due to several features offered by Bayesian network. Due to these advantages, Bayesian network approach was adopted in this study to estimate the probability of pipeline risk factors.…”

Section: Introductionmentioning

confidence: 99%

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Probability analysis of damages to subsea pipeline

Sulaiman

Tan

2019

Process Safety Progress

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Pipeline failure due to various threats may contribute to significant adverse impact on human safety, environment, and economy. In order to mitigate the severity of pipeline failure consequences, maintaining the integrity of the vast and aging pipeline structure is crucial. The main concern in offshore risk analysis is the unpredictable and uncertain pipeline conditions. In probability theory, Bayesian network is known as a powerful tool for knowledge presentation and condition inference under uncertainty. Probability analysis of pipeline damages is necessary to prevent unwanted incidents that may cause catastrophic accidents. In this paper, a Bayesian network model was developed to identify and analyze the probability of subsea pipeline condition subjected to corrosion, third party, operational, and material damages. Statistical data and experts' knowledge were integrated in addressing data limitation. Attaining the proposed network model, diagnostic analysis, mutual analysis, and sensitivity analysis were performed to validate and provide a substantial amount of confidence on the outcomes of the proposed model. The analyses have demonstrated that estimation of the developed model is reliable. The outcome obtained can be used to assist the decision maker to prepare preventive safety measures and allocate proper resources to significantly minimize the occurrence probability of the risk factors.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probability analysis of damages to subsea pipeline

Sulaiman

Tan

2019

Process Safety Progress

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“…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%

Developing a framework for dynamic risk assessment using Bayesian networks and reliability data

Kanes

Marengo

Abdel-Moati

et al. 2017

Journal of Loss Prevention in the Process Industries

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Process Safety in the oil and gas industry is managed through a robust Process Safety Management (PSM) system that involves the assessment of the risks associated with a facility in all steps of its life cycle. Risk levels tend to fluctuate throughout the life cycle of many processes due to several time varying risk factors (performances of the safety barriers, equipment conditions, staff competence, incidents history, etc.). While current practices for quantitative risk assessments (e.g. Bow-tie analysis, LOPA, etc.) have brought significant improvements in the management of major hazards, they are static in nature and do not fully take into account the dynamic nature of risk and how it improves risk-based decision making In an attempt to continually enhance the risk management in process facilities, the oil and gas industry has put in very significant efforts over the last decade toward the development of process safety key performance indicators (KPI or parameters to be observed) to continuously measure or gauge the efficiency of safety management systems and reduce the risks of major incidents. This has increased the sources of information that are used to assess risks in real-time. The use of such KPIs has proved to be a major step forward in the improvement of process safety in major hazards facilities. Looking toward the future, there appears to be an opportunity to use the multiple KPIs measured at a process plant to assess the quantitative measure of risk levels at the facility on a time-variant basis.ExxonMobil Research Qatar (EMRQ) has partnered with the Mary Kay O'Connor Process Safety Center -Qatar (MKOPSC-Q) to develop a methodology that establishes a framework for a tool that monitors in real time the potential increases in risk levels as a result of pre-identified risk factors that would include the use of KPIs (leading or lagging) as observations or evidence using Bayesian Belief Networks (BN).In this context, the paper presents a case study of quantitative risk assessment of a process unit using BN. The different steps of the development of the BN are detailed, including: translation of a Bowtie into a skeletal BBN, modification of the skeletal BN to incorporate KPIs (loss of primary containment (LOPC), equipment, management and human related), and testing of the BBN with forward and backward inferences. The outcomes of the dynamic modeling of the BN with real time insertion of evidence are discussed and recommendation for the framework for a dynamic risk assessment tool are made.

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“…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%

Prospect Prediction Model Of Indonesian Telematics Medium Large Size Enterprises Using Deep Learning Approach

Tosida¹,

Wihartiko²,

Utep³

et al. 2021

ijeeit

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Analysis of business prospects is an important part of predicting a country's economic conditions. Currently, the prediction of prospects for medium-big sized enterprises (MLE) in the telematics sector has not been widely researched and represented as a factor of economic development in Indonesia. In fact, in accordance with the development of the Industrial Revolution 4.0, the telematics sector business is one of the pillars that is a priority to be developed in Indonesia. The main purpose of this study is to construct the prediction model for prospects in the Indonesian telematics LME sector using a deep learning approach. We used data from the 2016 National Economic Census as many as 2500 preprocessed data. The deep learning approach in this study used a multilayer perceptrón (MLP) architecture, 17 attributes, 3 hidden layers and 5 target classes. The attributes in question include province, business owner education, legal entity status, length of operation, business network, total assets, business lava, number of workers, difficulties, partnerships, marketing innovations, comparison of profit with the previous year, and development plans. The target class of prospects are excellent, good, neutral, bad and very bad. The optimal results were achieved in epoch 50 conditions with a learning reate of 0.2 and an accuracy rate of 98.80%. Based on the prediction model, this business prospect can be used as a reference for the development of MLE in the telematics sector in Indonesia. This prospect model still lacks visualization and attribute analysis that affects the classification of prospects for Indonesian telematics MLE. Research development opportunities can be carried out through the integration of the whitebox model in the deep learning model and complementing a web-based graphical user interface (GUI) to make it easier for stakeholders to develop strategies based on the strength of attributes that affect the prospects for MLE Telematics Indonesia. This is expected to boost the competitiveness of the prospects for Indonesian telematics MLE.

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A DBN-based risk assessment model for prediction and diagnosis of offshore drilling incidents

Cited by 69 publications

References 31 publications

Probability analysis of damages to subsea pipeline

Probability analysis of damages to subsea pipeline

Developing a framework for dynamic risk assessment using Bayesian networks and reliability data

Prospect Prediction Model Of Indonesian Telematics Medium Large Size Enterprises Using Deep Learning Approach

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