TY - CHAP
T1 - Risk assessment of CO2 injection processes and storage
AU - Ribeiro e Sousa, L.
AU - Leal e Sousa, R.
AU - Vargas, Eurípedes
AU - Velloso, Raquel
AU - Karam, Karim
N1 - Publisher Copyright:
© 2017 Taylor & Francis Group, London, UK.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Different options for carbon capture, utilization and storage (CCUS) technologies exist and have been or are in the process of being implemented worldwide. With recent technological advancements, large scale CCUS schemes have become economically viable particularly when the captured CO2 is used in the enhancement of oil (EOR) or gas (EGR) recovery, or when government incentives such as carbon credits are in place. CO2 can also be stored in coal beds, in unminable coal seams and well-sealed abandoned coal mines. In this chapter, the risks associated with CO2 storage are described by identifying and examining different types of hazards. The importance of modeling and monitoring is emphasized and different methodologies to assess risk are discussed. There are a number of models available for data analysis, representation and risk analysis. Bayesian networks (BN) are described in detail, and some applications of BNs in CO2 injection and storage are presented for different hazard scenarios. Finally, several conclusions are drawn.
AB - Different options for carbon capture, utilization and storage (CCUS) technologies exist and have been or are in the process of being implemented worldwide. With recent technological advancements, large scale CCUS schemes have become economically viable particularly when the captured CO2 is used in the enhancement of oil (EOR) or gas (EGR) recovery, or when government incentives such as carbon credits are in place. CO2 can also be stored in coal beds, in unminable coal seams and well-sealed abandoned coal mines. In this chapter, the risks associated with CO2 storage are described by identifying and examining different types of hazards. The importance of modeling and monitoring is emphasized and different methodologies to assess risk are discussed. There are a number of models available for data analysis, representation and risk analysis. Bayesian networks (BN) are described in detail, and some applications of BNs in CO2 injection and storage are presented for different hazard scenarios. Finally, several conclusions are drawn.
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U2 - 10.1201/b20402
DO - 10.1201/b20402
M3 - Chapter
AN - SCOPUS:85052766160
SN - 9781138027619
SP - 359
EP - 397
BT - Rock Mechanics and Engineering Volume 3
ER -