Aplicação do filtro de Kalman por conjuntos de amostra à calibração e à validação de modelos previsionais de reservatórios de petróleo

• Ano de defesa: 2018
• Autor(a) principal: Caballero Pérez, Edison Fabián
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio de Janeiro; Brasil; Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia; Programa de Pós-Graduação em Engenharia Mecânica; UFRJ
• Palavras-chave em Português: Assimilação de dados; EnKF; CNPQ::ENGENHARIAS::ENGENHARIA MECANICA
• Link de acesso: http://hdl.handle.net/11422/12061

In this work we construct a new methodology for enhancing the predictive ac curacy of sequential methods for coupling flow and geomechanics whilst preserving low computational cost. The new computational approach is developed within the framework of the fixed stress split algorithm procedure in conjunction with data assimilation based on the Ensemble Kalman Filter (EnKF). In this context we identify tite high fidelity model with the two-way formulation where additional source term appears in the flow equation containing the time derivative of total nican stress. The iterative scheme is then interlaced with data assimilation steps which also incorporate the modeling error inherent to the EnKF framework. Such a procedure give rise to an “Enhanced one-way formulation” exhibiting substantial improvement in accuracy compared to the classical one-way method. The governing equations are discretized by mixed finite elements, and numerical simulation of a 2D slab problem between injection and production wells illustrate the tremendous achievement of the method proposed herein.