Generalized polynomial chaos expansion for fast and accurate uncertainty quantification in geomechanical modelling
C. Zoccarato, L. Gazzola, M. Ferronato, P. Teatini
Dept. of Civile, Environmental and Architectural Engineering,
University of Padova, Padova, Italy
ABSTRACT
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.
