DEPARTAMENTO DE MATEMÁTICA – CFM / UFSC

Seminário de Otimização & Problemas Inversos

Título: A combined Markov Chain Monte Carlo and Levenberg–Marquardt inversion method for heterogeneous subsurface reservoir modeling

Palestrante: Juarez dos Santos Azevedo (UFBA/UFSC)

Resumo: In this study, we systematically investigate an inverse method for heterogeneous porous media to obtain porosity and permeability fields considering only production well data. The forward modeling of the input data, namely pressure and saturation fields, is based on the motion equations of a coupled Darcy flow system involving two phases of isothermal fluid flow. We discretize these equations using a multiscale finite volume simulation technique in the spatial domain, and the backward Euler method in time domain. In the inversion procedure, we combine a global optimization method, the Markov CE.Krukoskihain Monte Carlo (MCMC) method, with a local optimization method, the Levenberg–Marquardt (LM) method. The MCMC was implemented as the Random Walk algorithm, and to generate the samples of the porosity and permeability fields, we employed Karhunen–Loève (KL) expansion of second-order stationary fields with Gaussian covariance. The coefficients of the KL expansion are estimated by minimizing the norm of the residual dependent on these fields. At the end of the MCMC iterations, we refine the KL coefficients associated with the porosity and permeability fields using the LM method. We verify in the numerical experiments that the accuracy of the porosity and permeability fields is improved by the LM refinement step.

Data: Segunda-feira,  02 de junho , 14h
Local: Auditório Airton Silva, Departamento de Matemática – MTM /CFM

Maiores informações:  http://mtm.ufsc.br/~maicon/seminar

E.Krukoski

Seminários de Otimização

Local convergence of Levenberg-Marquardt methods for nonzero-residue nonlinear least-squares problems under an error bound condition

Expositor: Douglas S. Gonçalves (UFSC)

Resumo: The Levenberg-Marquardt method (LM) is widely used for solving nonlinear systems of equations, as well as nonlinear least-squares problems. In this study, we consider local convergence issues of the LM method when applied to nonzero-residue nonlinear least-squares problems under an error bound condition, which is weaker than requiring full-rank of the Jacobian in a neighborhood of a stationary point. Differently from the zero-residue case, the choice of the LM parameter is shown to be dictated by (i)~the behavior of the rank of the Jacobian, and (ii)~a combined measure of nonlinearity and residue size in a neighborhood of the set of (possibly non-isolated) stationary points of the sum of squares function.

Data: Sexta-feira, 31 de agosto, 10h30m
Local: Auditório Airton Silva, sala MTM007 do Departamento de Matemática.

Maiores informações: www.mtm.ufsc.br/~maicon/seminar

E. Krukoski