Validação de testes de produção de poços de petróleo baseada em mineração de dados

• Ano de defesa: 2019
• Autor(a) principal: Duque, Maria Clara Machado de Almeida.
• Tipo de documento: Dissertação
• 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 de Produção; UFRJ
• Palavras-chave em Português: Estudos de validação; Mineração de dados; Produção de petróleo; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: http://hdl.handle.net/11422/13265

During production of petroleum fields, production tests are frequently conducted in each well to identify the current conditions of well production. After tests are done, they are evaluated by the responsible team and, according to the information obtained, can be validated or not. The objective of this work is to create production validation tools based on data mining to assist real-time validation process. The proposed methodology is divided into three main stages. In the first one, a preprocessing is done to identify anomalous data, using the Local Outlier Factor (LOF), modified Z-score and average distances. After that, in the second step, predictive models of classification are analyzed to characterize production test as valid and not valid, according to information of the production history of the well. In third step, regression models are applied to predict the oil, water and gas flow variables. In this part, a prediction interval for each variable is constructed using the bootstrap sampling technique. The proposed methodology was applied in 13 representative wells of a Brazilian oil field. The developed techniques collaborate with the decision-making process in oil production activities.