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Article Dans Une Revue Reliability Engineering and System Safety Année : 2022

A Bayesian Belief Network Model for the Risk Assessment and Management of Premature Screen-Out during Hydraulic Fracturing

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Résumé

Hydraulic fracturing is a well completion technique for Oil and Gas production enhancement in both conventional and unconventional reservoirs. However, it can result in the unfavorable consequence of the premature screen-out, which occurs due to the proppant bridging across the perforations or similar restricted flow areas. The objective of this work is to propose a novel framework of analysis that enables to quantify the risk of screen-out occurrence, to identify the riskiest scenarios and to determine the best risk mitigation strategies. The premature screen-out problem is addressed within a Risk Management and Control Process, wherein the qualitative and quantitative assessments of the early screen-out risk are performed by a Features, Events and Processes Analysis structured with a Bayesian Belief Network. The BBN probabilities are subject to a thorough uncertainty and sensitivity analysis. Sensitivity analysis is performed by the Sobol's variance decomposition method and the identified most influential probabilities of the BBN are re-estimated in order to reduce the output uncertainty. Finally, risk mitigation plans are formulated using risk importance measures to identify the riskiest scenarios and cost-benefit analysis to determine the optimal risk reduction actions The developed framework has been applied to a case study of vertical wells.
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Dates et versions

hal-03907659 , version 1 (20-12-2022)

Identifiants

Citer

Enrico Zio, Maryam Mustafayeva, Andrea Montanaro. A Bayesian Belief Network Model for the Risk Assessment and Management of Premature Screen-Out during Hydraulic Fracturing. Reliability Engineering and System Safety, 2022, 218, pp.108094. ⟨10.1016/j.ress.2021.108094⟩. ⟨hal-03907659⟩
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