Use of the subsurface has become steadily more intensive and globalized. This is due to wider application of conventional uses and to the emergence of relatively new, unconventional uses. This raises the question of whether we are managing the subsurface space in a sustainable way that also enables us to understand and mitigate the risks that may emerge. Depending on the purpose of subsurface space use, typical risk challenges may include e.g. impacts to groundwater, oil and gas resources. Expanding uses of the subsurface motivates additional effort to mitigate well-known and relatively new risks. Estimating risk levels in complex systems can be a daunting task if the strategy is to construct simulation models of all known physical processes combined with uncertainties in the system, which for subsurface projects, often dominate the system description. An alternative approach described here isolates the main risk drivers in a high-level probabilistic format known as a Bayesian (Belief) Network (BN). The BN approach accommodates more general relationships between uncertain variables than event or fault trees and allows expression of probabilities to consistently influence the top-level risk indicators. A BN risk model will typically be more compact and legible than its fault/event tree equivalent.


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