Optimizing strategies in the Oil and Gas Industry, particularly within reservoir engineering and management, remains a significant challenge due to the prohibitive computational time costs and high resource demands of current simulation methods, even for medium-sized reservoirs. Notably, existing scientific approaches have not leveraged Deep Neural Networks (DNNs) for fine-grained predictions at the grid-cell level. This thesis introduces a novel approach that integrates DNNs with a Design of Experiments framework to develop a proxy model for reservoir simulation software. The methodology includes a robust feature selection process, model design, and training strategy, supplemented by comprehensive statistical evaluations and graphical tools. The proposed proxy models are validated using four distinct industrial scenarios based on the SPE9 black oil benchmark, incorporating production and injection wells across diverse temporal samples. The results demonstrate a significant improvement in computational efficiency without compromising accuracy, achieving over 80% accuracy across all scenarios, and reaching up to 99.9% under specific conditions. These findings highlight the potential of DNN-based proxy models to transform reservoir management practices, offering a scalable and resource-efficient alternative to traditional numerical simulations.

Directional drilling can be seen as a well-established technology that has advanced in the past few years, allowing higher productivity in petroleum exploration. On the other hand, the design of optimal multi-directional drilling paths has not gained much attention in the literature. Existing models and algorithms for the optimisation of directional drilling paths often disregard important petrophysical attributes or apply heuristic methods, which cannot guarantee the optimality of the generated solutions. In this paper, we employ mixed-integer programming (MIP) to optimize multiple directional drilling paths. This is achieved by integrating a screening step, capable of identifying the most promising target regions in terms of flow capacity, with an MIP model responsible for selecting and sequencing the identified targets. Our approach considers several constraints such as drift angles, maximum wellbore length, and minimum safety distances. In addition, a branch-and-cut algorithm is proposed for solving real-world multi-directional instances of challenging sizes. We carry out a case study in the Campos Basin to validate the proposed models. Preliminary results show that the optimized drilling paths have superior performance when compared to the historical average recovery factor of the Campos Basin.

O problema inverso do potencial consiste em reconstruir uma fonte desconhecida com suporte em um domínio geométrico a partir de uma única medição sobre a fronteira. A fim de tratar este problema inverso mal posto, este será reescrito como um problema de otimização onde um funcional de forma baseado no critério de Kohn-Vogelius é minimizado. Este funcional mede a diferença entre as soluções de dois problemas auxiliares, onde um deles contém a informação relativa a` leitura no contorno enquanto o outro e´ munido com a informação correspondente à excitação no contorno. As soluções dos problemas auxiliares coincidem quando se está sobre a solução do problema inverso. Para minimizar o critério de Kohn-Vogelius, sua variação total com respeito a um conjunto de fontes concentradas é avaliada explicitamente. Baseado na expressão obtida, constrói-se um novo método para resolver o problema inverso do potencial. Por fim, alguns resultados numéricos são apresentados a fim de mostrar a efetividade do algoritmo de reconstrução construído.