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Quantitative Assessment Of Failure Probability Of Underground Natural Gas Storage Wells Using An Integrated Bow-Tie Bayesian Network Approach

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Underground natural gas storage (UGS) is an important component of the overall natural gas transportation and distribution system. It enables the utilities to supply natural gas during high seasonal demand periods and store gas during periods of lower demand. There are approximately 627 underground gas storage sites worldwide with a working gas capacity of 319.3 Billion m3 ( about 11.8 Trillion Cubic feet). The U.S. has a total of 414 natural gas storage fields, out of which 25 are inactive.

Product Number: 51322-17849-SG
Author: Francois Ayello, Narasi Sridhar, Arun Agarwal, Vincent Demay
Publication Date: 2022
Industries: Corrosion Monitoring and Control , Coatings and Linings , Coatings
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The storage of natural gas in underground reservoirs is an important component of the overall natural gas delivery infrastructure because it permits better management of the supply and demand cycles. Leakage of natural gas to the outside can lead to severe safety and environmental consequences. An integrated Bowtie (BT) and Bayesian Network (BN) model to assess the probability of gas release is presented in this paper. A barrier-based risk management approach, incorporated in the BT model, provides a useful visualization of the operational hazards and their safe management. The BT approach involves the identification of hazards (or threats) leading to a top event, such as release of natural gas to the external environment. Each hazard has several associated barriers that can either prevent the occurrence of the top event or mitigate the consequences. BT models were constructed for well head and sub-surface systems. However, the BT approach does not consider the interactions between different hazards and barriers. Furthermore, the degradation of the barrier effectiveness over time and space is typically not quantified. BN models were constructed to quantify the failure probabilities of the barriers identified by BT. The BN models compute the probabilities of failure of the wellhead and sub-surface systems. Sensitivity and value of information analyses were conducted using the BN model.

The storage of natural gas in underground reservoirs is an important component of the overall natural gas delivery infrastructure because it permits better management of the supply and demand cycles. Leakage of natural gas to the outside can lead to severe safety and environmental consequences. An integrated Bowtie (BT) and Bayesian Network (BN) model to assess the probability of gas release is presented in this paper. A barrier-based risk management approach, incorporated in the BT model, provides a useful visualization of the operational hazards and their safe management. The BT approach involves the identification of hazards (or threats) leading to a top event, such as release of natural gas to the external environment. Each hazard has several associated barriers that can either prevent the occurrence of the top event or mitigate the consequences. BT models were constructed for well head and sub-surface systems. However, the BT approach does not consider the interactions between different hazards and barriers. Furthermore, the degradation of the barrier effectiveness over time and space is typically not quantified. BN models were constructed to quantify the failure probabilities of the barriers identified by BT. The BN models compute the probabilities of failure of the wellhead and sub-surface systems. Sensitivity and value of information analyses were conducted using the BN model.

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