Impact of multiple uncertainties on gravimetric variations across randomly heterogeneous aquifers during pumping

Maina, Fadji Zaouna; Guadagnini, Alberto; Riva, Mònica

doi:10.1016/j.advwatres.2021.103978

Cited by 4 publications

(2 citation statements)

References 67 publications

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“…Here, we rely on a Quasi-Monte Carlo approach (e.g., Caflisch 1998;Niederreiter 1992;Feil 2009;Maina et al 2021) to sample model parameter values within the corresponding supports illustrated in Sect. 2.2.…”

Section: Characterization Of the Pce Surrogate Modelsmentioning

confidence: 99%

Sensitivity-based Parameter Calibration of Single- and Dual-continuum Coreflooding Simulation Models

et al. 2022

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Our study is keyed to the development of a viable framework for the stochastic characterization of coreflooding simulation models under two- and three-phase flow conditions taking place within a core sample in the presence of preferential flow of the kind that can be associated with the presence of a system of fractures. We do so considering various modeling strategies based on (spatially homogeneous or heterogeneous) single- and dual-continuum formulations of black-oil computational models and relying on a global sensitivity-driven stochastic parameter calibration. The latter is constrained through a set of data collected under a water alternating gas scenario implemented in laboratory-scale coreflooding experiments. We set up a collection of Monte Carlo (MC) numerical simulations while considering uncertainty encompassing (a) rock attributes (i.e., porosity and absolute permeability), as well as (b) fluid–fluid/ fluid–solid interactions, as reflected through characteristic parameters of relative permeability and capillary pressure formulations. Modern moment-based global sensitivity indices are evaluated on the basis of the MC model responses, with the aim of (i) quantifying sensitivity of the coreflooding simulation results to variations of the input uncertain model parameters and (ii) assessing the possibility of reducing the dimensionality of model parameter spaces. We then rest on a stochastic inverse modeling approach grounded on the acceptance–rejection sampling (ARS) algorithm to obtain probability distributions of the key model parameters (as identified through our global sensitivity analyses) conditional to the available experimental observations. The relative skill of the various candidate models to represent the system behavior is quantified upon relying on the deviance information criterion. Our findings reveal that amongst all tested models, a dual-continuum formulation provides the best performance considering the experimental observations available. Only a few of the parameters embedded in the dual-continuum formulation are identified as major elements significantly affecting the prediction (and associated uncertainty) of model outputs, petrophysical attributes and relative permeability model parameters having a stronger effect than parameters related to capillary pressure.

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Section: Characterization Of the Pce Surrogate Modelsmentioning

confidence: 99%

Sensitivity-based Parameter Calibration of Single- and Dual-continuum Coreflooding Simulation Models

et al. 2022

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“…As such, this comprehensive approach yields information enabling us to characterize various aspects of uncertainty, without being limited solely to the concept of variance. The ensuing indices (termed AMA indices, after the initials of the authors (Dell'Oca et al 2017)) have been effectively employed in a variety of contexts, including geophysical analyses related to gravimetric responses due to pumping tests (Maina et al 2021), biochemical degradation of compounds such as glyphosate in soils (la Cecilia et al 2020), and groundwater flow, including its feedbacks with evapotranspiration (Bianchi Janetti et al 2019;Maina and Siirila-Woodburn 2020).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis and Quantification of the Role of Governing Transport Mechanisms and Parameters in a Gas Flow Model for Low-Permeability Porous Media

Sandoval

Riva

Colombo³

et al. 2022

Transp Porous Med

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Recent models represent gas (methane) migration in low-permeability media as a weighted sum of various contributions, each associated with a given flow regime. These models typically embed numerous chemical/physical parameters that cannot be easily and unambiguously evaluated via experimental investigations. In this context, modern sensitivity analysis techniques enable us to diagnose the behavior of a given model through the quantification of the importance and role of model input uncertainties with respect to a target model output. Here, we rely on two global sensitivity analysis approaches and metrics (i.e., variance-based Sobol’ indices and moment-based AMA indices) to assess the behavior of a recent interpretive model that conceptualizes gas migration as the sum of a surface diffusion mechanism and two weighted bulk flow components. We quantitatively investigate the impact of (i) each uncertain model parameter and (ii) the type of their associated probability distribution on the evaluation of methane flow. We then derive the structure of an effective diffusion coefficient embedding all complex mechanisms of the model considered and allowing quantification of the relative contribution of each flow mechanism to the overall gas flow.

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Solving subsurface flow toward wells in layered soils using hybrid method of fundamental solutions

Liu

Hong

2023

Engineering Analysis with Boundary Elements

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Impact of multiple uncertainties on gravimetric variations across randomly heterogeneous aquifers during pumping

Cited by 4 publications

References 67 publications

Sensitivity-based Parameter Calibration of Single- and Dual-continuum Coreflooding Simulation Models

Sensitivity-based Parameter Calibration of Single- and Dual-continuum Coreflooding Simulation Models

Sensitivity Analysis and Quantification of the Role of Governing Transport Mechanisms and Parameters in a Gas Flow Model for Low-Permeability Porous Media

Solving subsurface flow toward wells in layered soils using hybrid method of fundamental solutions

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