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Open AccessArticle

Generalized Polynomial Chaos Expansion for Fast and Accurate Uncertainty Quantification in Geomechanical Modelling

Department of Civil, Environmental and Architectural Engineering, University of Padova, 35131 Padova, Italy
Author to whom correspondence should be addressed.
Algorithms 2020, 13(7), 156;
Received: 29 April 2020 / Revised: 17 June 2020 / Accepted: 24 June 2020 / Published: 30 June 2020
Geomechanical modelling of the processes associated to the exploitation of subsurface resources, such as land subsidence or triggered/induced seismicity, is a common practice of major interest. The prediction reliability depends on different sources of uncertainty, such as the parameterization of the constitutive model characterizing the deep rock behaviour. In this study, we focus on a Sobol’-based sensitivity analysis and uncertainty reduction via assimilation of land deformations. A synthetic test case application on a deep hydrocarbon reservoir is considered, where land settlements are predicted with the aid of a 3-D Finite Element (FE) model. Data assimilation is performed via the Ensemble Smoother (ES) technique and its variation in the form of Multiple Data Assimilation (ES-MDA). However, the ES convergence is guaranteed with a large number of Monte Carlo (MC) simulations, that may be computationally infeasible in large scale and complex systems. For this reason, a surrogate model based on the generalized Polynomial Chaos Expansion (gPCE) is proposed as an approximation of the forward problem. This approach allows to efficiently compute the Sobol’ indices for the sensitivity analysis and greatly reduce the computational cost of the original ES and MDA formulations, also enhancing the accuracy of the overall prediction process. View Full-Text
Keywords: geomechanical modelling; polynomial chaos expansion; bayesian update geomechanical modelling; polynomial chaos expansion; bayesian update
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Zoccarato, C.; Gazzola, L.; Ferronato, M.; Teatini, P. Generalized Polynomial Chaos Expansion for Fast and Accurate Uncertainty Quantification in Geomechanical Modelling. Algorithms 2020, 13, 156.

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