A surrogate model for fast land subsidence prediction and uncertainty quantification
C. Zoccarato, M. Ferronato, P. Teatini
Dept. of Civil, Environmental and Architectural Engineering, University of Padova, Italy
ABSTRACT
Numerical modeling of anthropogenic land subsidence due to the exploitation of subsurface
resources is of major interest to anticipate possible environmental impacts on the ground
surface. The reliability of predictions depends on different sources of uncertainty introduced
into the modeling procedure. In this study, we focus on reduction of model parameter
uncertainty via assimilation of land surface displacements. A test case application on a
deep hydrocarbon reservoir is considered where land settlements are predicted with the aid
of a 3D Finite Element (FE) model. The calibration of the parameters defining the rock
constitutive law is obtained by the Ensemble Smoother (ES) technique. The ES convergence
is guaranteed with a large number of Monte Carlo simulations that may be computationally
infeasible in large scale and complex systems. A surrogate model based on the generalized
Polynomial Chaos Expansion (gPCE) is proposed as an approximation of the forward problem.
This approach is expected to reduce the overall computational cost of the original ES
formulation and enhance the accuracy of the parameter estimation problem. The result is
compared with a posterior sampling by Markov Chain Monte Carlo (MCMC) to assess the quality
of the assimilation.
