Search

The reactivation of faults and the generation of fractures can be caused by stress changes due to injection and/or production of fluids into and/or from the subsurface. The simulation of these processes, which could be associated with (micro-)seismicity, is affected by a high uncertainty. The aim of this work is at developing a mathematical framework to quantify and possibly reduce the prior modeling uncertainties by assimilation of seismic data. The mechanics of fault (re-)activation is simulated by a Finite Element (FE) numerical model where the discontinuous displacements between the fault surfaces are suitably considered using appropriate Interface Elements (IEs). The study is carried out by using a stochastic approach, with a global sensitivity analysis (gSA) based on Sobol' indices initially performed to estimate the influence of the input parameters on the model solution. Then, a Markov Chain Monte Carlo (MCMC) sampling technique based on the generalized Polynomial Chaos expansion (gPC) surrogate solution is used to update the prior information conditioned on seismic observations. The methodology is tested on a 3D synthetic test case. The uncertain input is the natural stress regime and the Mohr-Coulomb parameters characterizing the fault activation criterion. A good reduction of the prior uncertainty is obtained, showing that the assimilation of seismic data can have a promising potential for improving the subsurface characterization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]