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(IOR/EOR): Predições E Análises Estratégicas Por Meio De Algoritmos De Inteligência Artificial

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(IOR/EOR): Predições E Análises Estratégicas Por Meio De Algoritmos De Inteligência Artificial LUIZ FELIPE NIEDERMAIER CUSTÓDIO Integração mundial de bases de dados de Improved and Enhanced Oil Recovery (IOR/EOR): predições e análises estratégicas por meio de algoritmos de Inteligência Artificial Área de concentração: Engenharia de Petróleo Orientador: Prof. Dr. Cleyton de Carvalho Carneiro Co-orientador: Prof. Dr. Rafael dos Santos Gioria Colaboradores: Profs. Drs. Marcio Augusto Sampaio Pinto, Caetano Rodrigues Miranda e Jean Vicente Ferrai São Paulo 2019 LUIZ FELIPE NIEDERMAIER CUSTÓDIO Integração mundial de bases de dados de Improved and Enhanced Oil Recovery (IOR/EOR): predições e análises estratégicas por meio de algoritmos de Inteligência Artificial Monografia apresentada à Escola Politécnica da Universidade de São Paulo para obtenção do título de Engenheiro de Petróleo. Área de concentração: Engenharia de Petróleo Orientador: Prof. Dr. Cleyton de Carvalho Carneiro Co-orientador: Prof. Dr. Rafael dos Santos Gioria Colaboradores: Prof. Dr. Marcio Augusto Sampaio Pinto e Prof. Dr. Jean Vicente Santos 2019 Este exemplar foi revisado e alterado em relação à versão original, sob responsabilidade única do autor e com a anuência de seu orientador. São Paulo, Assinatura do autor ____________________________ Assinatura do orientador _______________________ Catalogação-na-Publicação Luiz Felipe Niedermaier Custódio Integração mundial de bases de dados de Improved and Enhanced Oil Recovery (IOR/EOR): predições e análises estratégicas por meio de algoritmos de Inteligência Artificial / L. F. N. C. -- São Paulo, 2019. 157 p. Trabalho de Formatura - Escola Politécnica da Universidade de São Paulo. Departamento de Engenharia de Minas e de Petróleo. 1.Enhanced Oil Recovery 2.Improved Oil Recovery 3.Artificial Intelligence 4.Data Management 5.Screening I.Universidade de São Paulo. Escola Politécnica. Departamento de Engenharia de Minas e de Petróleo II.t. À minha família. AGRADECIMENTOS Gostaria de, em primeiro lugar, agradecer profundamente a toda a minha família que, sem dúvidas, foi de extrema importância durante a minha graduação. Palavras não descrevem o quanto eu simplesmente amo vocês. Em segundo lugar, agradeço aos meus orientadores professores Dr. Cleyton de Carvalho Carneiro e Dr. Rafael dos Santos Gioria, por abraçarem e acreditarem na minha ideia e acompanharem de perto o desenvolvimento desse trabalho, além de me incentivarem a tornar isso uma iniciativa empreendedora, que sei ainda ter muito o que melhorar. Sou muito grato também aos professores Dr. Jean Vicenti Ferrari e Dr. Marcio Augusto Sampaio por auxílio nas análises e em insights diversos. Tenho grande apreço por tudo que aprendi com os senhores, em que o profissionalismo, a vontade de ensinar, as vossas conquistas e as intensas lutas como docentes do curso de Engenharia de Petróleo na Poli-USP em Santos são fascinantes e exemplares. Obrigado também ao Prof. Dr. Caetano Rodrigues Miranda pela oportunidade de Iniciação Científica junto ao grupo NanoPetro ainda no segundo ano da graduação, e por agora nos últimos meses iniciarmos uma abordagem de integração entre os resultados desse TCC com predições via dinâmica molecular, que acredito que possa dar bons frutos no futuro. Agradeço ainda aos colegas do LASG/InTRA pelo companheirismo nas infinitas horas de labuta (e descontração também) na rotina do laboratório. A agradeço também à ajuda com feedbacks, opiniões e críticas diversas que pude obter por parte do Arthur Henrique, do Eng. Bruno Schaefer com sua experiência de trabalho em campo offshore, do M. Sc. Paulo Ranazzi com sua bagagem científica crítica e de todos os demais da Poli Santos que, de alguma forma ou outra, me ajudaram. Sou muito grato também por ao longo dessa trajetória ter conquistado a amizade de grandes pessoas e que hoje se tornaram excelentes profissionais como Gabriel André Silvério, Milena Christy, Jonathan Mendes, Crhis Moreira, Douglas Barbosa, Lucas Renan da Silva, Fernando Ferreira Silva, Bárbara Leliard, Rafael Tavares, Cassia Oliveira, Fernando Bonsucesso, Maryam Furlan, Leticia Santos, dentre outros com os quais pude contar em diversos momentos e poder partilhar muitas risadas e conversas falando desde sobre questões existenciais até futilidades da vida. Nesse ínterim, certamente vocês se tornaram um alicerce fundamental para constituir quem eu sou hoje. Por fim, gostaria de agradecer à Escola Politécnica da USP e a todos os contribuintes do Estado de São Paulo por, indiretamente ou não, manterem uma universidade pública e de qualidade e por eu ter sido contemplado com bolsas de estudo ao longo da faculdade. Sem dúvida esses anos foram extremamente intensos e produtivos, e sinto que o que aprendi aqui permitiu-me caminhar um passo além na curva de aprendizado da vida e ver que, principalmente, tenho ainda muito o que aprender e retornar à sociedade por esse investimento em minha formação. “Life is a play that does not allow testing. So, sing, cry, dance, laugh and live intensely, before the curtain closes and the piece ends with no applause” Charles Chaplin RESUMO Os métodos de Improved e Enhanced Oil Recovery (IOR/EOR) apresentam ampla relevância para garantir melhor eficiência na recuperação de petróleo. O desenvolvimento e aplicação de algoritmos de Inteligência Artificial, por outro lado, tem sido cada vez mais difundido. A partir de algoritmos de Machine Learning aplicados a uma base de dados mundial, este trabalho visa observar a dinâmica das variáveis que influenciam nos métodos de IOR/EOR, além de desenvolver uma sistemática de predição (screening) dos métodos de IOR/EOR mais adequados para cada campo de produção. Uma base de dados foi contruída totalizando 1035 projetos de IOR/EOR, localizados em 28 países ao redor do mundo, mais de 30 métodos de IOR/EOR e 323 variáveis. A compilação dos dados foi feita a partir de artigos científicos, relatórios de órgãos especializados, além de apresentações para investidores de companhias operadoras, dentre outras fontes. A base de dados resultante, inédita contém seis principais conjuntos de variáveis para cada reservatório: (i) registros de localização; (ii) métodos de recuperação envolvidos; (iii) propriedades geológicas e petrofísicas dos reservatórios; (iv) níveis de maturidade e lucratividade; (v) fator de recuperação e propriedades termodinâmicas; (vi) empresas operadoras e demais particularidades. Após etapas de pré-processamento incluindo uniformização, ajustes e limpeza das variáveis, foram executadas análises por redes neurais artificiais não supervisionadas, a partir de algoritmos de Self-Organizing Maps. Utilizaram-se: as component plots para observar interrelações entre variáveis; a imputação para a predição do método de IOR/EOR mais indicado para os campos de interesse; e a dissimilaridade do seno para obter-se reservatórios análogos. Tal integração de diversas bases de dados dos métodos de IOR/EOR em diferentes regiões do mundo como fonte de treinamento e aprendizado de redes neurais artificiais não supervisionadas é considerado inédito, possibilitando confirmar informações da literatura e trazer insights acerca das aplicações, vantagens e limitações de cada método de IOR/EOR. Os resultados do screening também trazem inovações: foi possível incluir preço do barril de petróleo e, de maneira implícita, fatores como disponibilidade de recursos e aspectos regulatórios, e validar o modelo com casos reais em que aspectos espacialmente próximos aos campos de produção mostraram ser mais relevantes na escolha do método de IOR/EOR do que exclusivamente propriedades petrofísicas dos reservatórios, tornando-se uma ferramenta relevante para embasar e agilizar as tomadas de decisões no desenvolvimento de um campo de produção. Palavras chave: Enhanced Oil Recovery; Improved Oil Recovery; Artificial Intelligence; Data Management; Screening. 8 ABSTRACT Enhanced and Enhanced Oil Recovery (IOR/EOR) methods consist of extremely relevant activities to ensure a better oil recovery economy. The development and application of Machine Learning algorithms, on the other hand, has been increasingly widespread. This study presents results from a worldwide integrative reservoir database in which Enhanced/Improved Oil Recovery (EOR/IOR) methods were applied. The goal was to investigate the influence of 323 variables related to EOR/IOR methods, using data from 1035 production oil reservoirs around the world. For this, it was developing a methodology using an artificial intelligence (AI) algorithm capable of screening the most appropriate EOR/IOR methods for each oilfield. The worldwide database integrated different sources with global, regional and local records, containing five sets of variables related to production fields: (i) location records; (ii) recovery methods involved; (iii) geological and core analysis properties of the reservoirs; (iv) levels of maturity, success, and profitability of methods and projects; and (v) recovery factor. Correlation and significance analyzes were used to identify and/or remove inappropriate variables. AI algorithm based on Self-Organizing Maps (SOM) was applied, resulting in maps used to observe the influence and relations of the variables. Cosine similarity was applied for screening EOR/IOR methods based on unidentified reservoir properties. The integration of several EOR/IOR methods from various regions of the world as a training and learning source of AI algorithms is considered an unprecedented approach. The SOM analysis confirmed information already supported by literature regarding reservoir engineering. It also provided insights about applications,
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