Modelagem matemático-numérico-computacional do transporte e deposição de sólidos em processo de perfuração em águas profundas

• Ano de defesa: 2023
• Autor(a) principal: Barbosa, Paulo Costa Porto de Figueiredo
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Engenharia Mecânica
• Palavras-chave em Português: DEM; CFD; MFSim; Petrobras; CNPQ::ENGENHARIAS::ENGENHARIA MECANICA::FENOMENOS DE TRANSPORTE::MECANICA DOS FLUIDOS; Engenharia mecânica
• Link de acesso: https://repositorio.ufu.br/handle/123456789/37665; http://doi.org/10.14393/ufu.te.2023.95

Computational fluid dynamics (CFD) is an important tool for understanding and analyzing multi-phase flows, which are commonly found in industrial processes such as oil and gas production, the chemical industry, and environmental system analysis. Through CFD, it is possible to create complex computational models that describe fluid flow to predict the behavior of real physical processes, and in some cases, computational simulations are more feasible than material experiments. Numerically, for gas-solid flows, it is common the particles be treated in a Lagrangian frame of reference while the fluid in an Eulerian frame of reference. In the context of the project "Study of Dispersion of Discarded Material Plumes in Offshore Operations", funded by Petrobras, this thesis aims at the computational development of a multi-phase flow modeling with solid particles (DEM) for computational simulation of the first stages of oil well drilling. In these stages, the drilling fluid and rock fragments are discarded on the ocean floor, causing damage to benthic organisms. This thesis proposes an investigation into the dynamics of the debris formed during the first phase of the oil well drilling process, taking into account the flow conditions, the interaction between the particles, and the dispersion of the discarded material. The proposed modeling will be important for understanding and predicting the behavior of multi-phase flows, helping to develop more efficient and sustainable processes and products.