Impact of Hydrogeological Uncertainty on Estimation of Environmental Risks Posed by Hydrocarbon Transportation Networks

Abstract

Ubiquitous hydrogeological uncertainty undermines the veracity of quantitative predictions of soil and groundwater contamination due to accidental hydrocarbon spills from onshore pipelines. Such predictions, therefore, must be accompanied by quantification of predictive uncertainty, especially when they are used for environmental risk assessment. We quantify the impact of parametric uncertainty on quantitative forecasting of temporal evolution of two key risk indices, volumes of unsaturated and saturated soil contaminated by a surface spill of light nonaqueous‐phase liquids. This is accomplished by treating the relevant uncertain parameters as random variables and deploying two alternative probabilistic models to estimate their effect on predictive uncertainty. A physics‐based model is solved with a stochastic collocation method and is supplemented by a global sensitivity analysis. A second model represents the quantities of interest as polynomials of random inputs and has a virtually negligible computational cost, which enables one to explore any number of risk‐related contamination scenarios. For a typical oil‐spill scenario, our method can be used to identify key flow and transport parameters affecting the risk indices, to elucidate texture‐dependent behavior of different soils, and to evaluate, with a degree of confidence specified by the decision‐maker, the extent of contamination and the correspondent remediation costs.

Characterizing the Influence of Multiple Uncertainties on Predictions of Contaminant Discharge in Groundwater Within a Lagrangian Stochastic Formulation

“…The key idea behind PCE is to approximate the response surface through an orthonormal polynomial basis in the parameter space to represent the model output to change in input parameters. PCE has been been employed for GSA (e.g., Crestaux et al, 2009) and has been applied to several hydrogeological problems (e.g., Ciriello et al, 2017, 2019; Oladyshkin et al, 2012). Specific applications of PCEs to address groundwater quality are also reported in the literature (Ciriello, Di Federico, et al, 2013; Oladyshkin et al, 2012; Riva et al, 2015).…”

Probabilistic Forecast of Single‐Phase Flow in Porous Media With Uncertain Properties

“…Numerical strategies aiming to outperform MCS in terms of computational efficiency include quasi-MC (Caflisch, 1998), multilevel MC (Giles et al, 2015), and various stochastic finite element methods (Xiu, 2010). While widely used in practice, including for subsurface-related applications (e.g., Ciriello et al, 2017;Dodwell et al, 2015;Liodakis et al, 2018; and the references therein), under certain conditions such methods 10.1029/2019WR026090 can be slower than MCS. For example, multilevel MC might become slower than regular MC when estimating a system state's distribution to the same accuracy (Giles et al, 2015), and polynomial chaos-based techniques have been shown to underperform MC if random parameter fields in (nonlinear) models exhibit short correlation lengths and/or high variances (Barajas-Solano & Tartakovsky, 2016).…”

Distribution‐Based Global Sensitivity Analysis in Hydrology

“…Its increasing popularity is linked to the use of surrogate models, which provide an easy‐to‐compute approximation to computationally demanding high‐fidelity models. One could argue that without such surrogates, GSA would be unfeasible for most, if not all, hydrologic modeling of practical significance (e.g., Ciriello, Di Federico, et al., , ). Polynomial chaos expansions (PCEs) (Ghanem & Spanos, ; Wiener, ) are widely used to construct surrogate models of subsurface processes (e.g., Ashraf et al., ; Deman et al., ; Marrel et al., ).…”