An assessment of probabilistic disaster in the oil and gas supply chain leveraging Bayesian belief network

Sakib, Nazmus; Hossain, Niamat Ullah Ibne; Nur, Farjana; Talluri, Srinivas; Jaradat, Raed; Lawrence, Jeanne Marie

doi:10.1016/j.ijpe.2021.108107

Cited by 70 publications

(39 citation statements)

References 41 publications

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“…The FR method obtains a prior probability, and then the BBN model is used to infer the posterior probability of the event. Compared with the node classification method based on the characteristics of the node itself in the previous studies ( Dai et al, 2021 ; Sakib et al, 2021 ), the FR method is graded the driving factors based on the importance of each attribute interval of the factor to the susceptibility of the event, which is more scientific. Therefore, as the premise of the BBN model interference, the FR model can provide a relatively reliable prior probability.…”

Section: Discussionmentioning

confidence: 99%

Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

Zhou

2021

Front. Plant Sci.

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Grasslands are crucial components of ecosystems. In recent years, owing to certain natural and socio-economic factors, alpine grassland ecosystems have experienced significant degradation. This study integrated the frequency ratio model (FR) and Bayesian belief networks (BBN) for grassland degradation risk assessment to mitigate several issues found in previous studies. Firstly, the identification of non-encroached degraded grasslands and shrub-encroached grasslands could help stakeholders more accurately understand the status of different types of alpine grassland degradation. In addition, the index discretization method based on the FR model can more accurately ascertain the relationship between grassland degradation and driving factors to improve the accuracy of results. On this basis, the application of BBN not only effectively expresses the complex causal relationships among various variables in the process of grassland degradation, but also solves the problem of identifying key factors and assessing grassland degradation risks under uncertain conditions caused by a lack of information. The obtained result showed that the accuracies based on the confusion matrix of the slope of NDVI change (NDVIs), shrub-encroached grasslands, and grassland degradation indicators in the BBN model were 85.27, 88.99, and 74.37%, respectively. The areas under the curve based on the ROC curve of NDVIs, shrub-encroached grasslands, and grassland degradation were 75.39% (P < 0.05), 66.57% (P < 0.05), and 66.11% (P < 0.05), respectively. Therefore, this model could be used to infer the probability of grassland degradation risk. The results obtained using the model showed that the area with a higher probability of degradation (P > 30%) was 2.22 million ha (15.94%), with 1.742 million ha (78.46%) based on NDVIs and 0.478 million ha (21.54%) based on shrub-encroached grasslands. Moreover, the higher probability of grassland degradation risk was mainly distributed in regions with lower vegetation coverage, lower temperatures, less potential evapotranspiration, and higher soil sand content. Our research can provide guidance for decision-makers when formulating scientific measures for alpine grassland restoration.

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Section: Discussionmentioning

confidence: 99%

Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

Zhou

2021

Front. Plant Sci.

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“…This can be realized using ML for activities such as process safety management (PSM), risk-based inspection (RBI), disaster assessments and production and maintenance related tasks. In these cases, ML has helped to expedite data collection and analysis processes in PSM, predict the vulnerability and disruption in disaster assessments, improve quality of conventional RBI by reducing output variability and increasing precision and accuracy [6,[45][46][47]. [48] discussed the use of natural language processing, to help with the acquisition of knowledge related to accidents throughout the supply chain.…”

Section: Other Applications Of Ai Along the Whole Oil And Gas Supply ...mentioning

confidence: 99%

“…NLP was used to improve data acquisition, to help discover causal accident relations from databases. Offshore Robotics Shukla and Karki (2016b) [37] Predict and assess disasters Machine learning -Supervised learning Sattari et al (2021) [45] Predict and assess disasters Machine learning -Supervised learning Sakib et al (2021) [46] Oil and Gas industry tasks Machine learning; Multi-agent Systems Hanga and Kovalchuk (2019) [6] Risk based inspection Machine learning Rachman and Ratnayake (2019) [47] Accident exploration Natural language processing Single et al (2020) [48]…”

Section: Descriptive Analysis For Ai Location and Technology Typementioning

confidence: 99%

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Deif

Vivek

2022

HighTech. Innov. J.

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The purpose of this paper is to gain a better understanding of Artificial Intelligence (AI) application dynamics in the oil and gas supply chain. A location perspective is used to explore the opportunities and challenges of specific AI technologies from upstream to downstream of the oil and gas supply chain. A literature review approach is adopted to capture representative research along these locations. This was followed by descriptive and comparative analysis for the reviewed literature. Results from the conducted analysis revealed important insights about AI implementation dynamics in the oil and gas industry. Furthermore, various recommendations for technology managers, policymakers, practitioners, and industry leaders in the oil and gas industry to ensure successful AI implementation were outlined. Doi: 10.28991/HIJ-SP2022-03-01 Full Text: PDF

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“…The concept of energy security taking into account three new perspectives: sovereignty, robustness and resilience in the gas sector of the European Union is explored in [39]. An example of a comprehensive approach to energy security is presented in [40] The topic of Bayesian networks appears in the context of optimization of gas supply reliability in natural gas pipelines [41] and an assessment of probabilistic disaster in the oil and gas supply chain [42]. However, there are no attempts to model the gas system with the use of probabilistic methods to obtain the possibility of forecasting the probability of certain events and variable values.…”

Section: Introductionmentioning

confidence: 99%

Application of Bayesian Networks in Modeling of Underground Gas Storage Energy Security

2022

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Energy security is a multidimensional and multifaceted concept, therefore defining it is a complex problem. It requires the consideration of a wide set of factors from the fields of economics, geology, ecology and geopolitics, all of which have an influence on energy security or the lack thereof. The article focuses on natural gas, which is a very specific fuel in the European context. It is the most “politicized” source of energy, as a consequence of its growing importance as a transition fuel in the energy transformation process. In order to identify dependencies between variables on the gas market and analyze their impact on it (in particular on underground storage), the authors chose a set of variables and built a Bayesian network. The network is an effective and flexible tool that allows analysis of the relationships between the variables that build them and model their values based on evidence. The article presents two stages of work with the Bayesian network. In the first one, a network was built based on historical data. It shows the relationships between the variables as well as the probability of the value ranges of individual variables. A huge advantage of the presented Bayesian network is that it can be used to model various scenarios on the gas market. Moreover, the ability to make statistical inferences for all its nodes represents a valuable additional feature. Several examples of such inferences are presented in the second stage of the analysis, examining the impact of consumption variability on the level of inventory in underground gas storage facilities, the impact of having an LNG terminal and the share of natural gas in electricity production on the storage capacity of a given country. The use of tools such as Bayesian networks allows us to better discover the interrelationships between variables influencing the energy market, analyze them, and estimate the impact on energy security of distinct scenarios described with specific metrics. A simple example of such a metric, i.e., the minimum level of gas storage at the end of the winter season, as well as its analysis and modeling using a relatively simple Bayesian network, is presented in this article.

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An assessment of probabilistic disaster in the oil and gas supply chain leveraging Bayesian belief network

Cited by 70 publications

References 41 publications

Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

Understanding AI Application Dynamics in Oil and Gas Supply Chain Management and Development: A Location Perspective

Application of Bayesian Networks in Modeling of Underground Gas Storage Energy Security

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