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.

Over the past decades, directional drilling has continuously advanced to increase hydrocarbon recovery by effectively targeting high-productivity reservoirs. However, many existing approaches primarily focus on heuristic optimization algorithms. Moreover, existing models often neglect the incorporation of petrophysical attributes that can significantly impact the selection of production targets, such as the reservoir quality indicator. This article introduces a novel application of mixed-integer programming to define directional drilling paths, considering practical aspects of interest. The paths are subject to drift angle constraints and reference coordinates that align with the optimal reservoir targets. Such targets are identified using the authors’ proposed technique of maximum closeness centrality and the geologic model of hydraulic flow units. In order to evaluate the effectiveness of this approach, a realistic model of the Campos Basin in Brazil is studied. The results reveal that the highest recovery factors obtained with the proposed methodology (17%) exceed the historical average recovery factor of the studied reservoir (15.66%). We believe this study can contribute to the ongoing efforts to enhance directional drilling and maximize the production potential of offshore oil and gas reservoirs.

This study deals with the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONFs) and Recurrences of Faults (RFs). To perform the predictions, they used predictive models based on machine learning, more specifically on MLP (Multi-Layer Perceptron) neural networks. The investigation unveiled that through the analysis of historical failure data and the consideration of variables including altitude, number of failures on the same street, and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes significantly to the field of water infrastructure management by proposing the applicability of a data-driven approach in diverse urban settings and across various types of infrastructure networks, including those pertaining to energy or communication. These conclusions underscore the paramount importance of systematic data collection and analysis in both averting failures and optimizing the allocation of resources within water utilities.

This study addresses the prediction of recurring failures in water supply networks, a complex and costly task, but essential for the effective maintenance of these vital infrastructures. Using historical failure data provided by Companhia de Água e Esgotos da Paraíba (CAGEPA), the research focuses on predicting the time until the next failure at specific points in the network. The authors divided the failures into two categories: Occurrences of New Faults (ONF) and Recurrences of Faults (RF). To make the predictions, they used predictive models based on Machine Learning, more specifically on MLP (Multi Layer Perceptron) neural networks. The study revealed that, by analyzing data from past failures and considering factors such as altitude, number of failures on the same street and days between failures, it is possible to achieve an accuracy greater than 80% in predicting failures within a 90-day interval. This demonstrates the feasibility of using fault history to predict future water supply outages with significant accuracy. These forecasts allow water utilities to plan and optimize their maintenance, minimizing inconvenience and losses. The article contributes to the field of water infrastructure management by suggesting that the data-driven approach can be applied in other cities or in networks of different types, such as energy or communication networks. The conclusions highlight the importance of data collection and analysis in preventing failures and optimizing water utilities’ resources.

Knowing the ultimate oil production in wells is a crucial point for reservoir planning and management to anticipate value for money. Commercial reservoir simulators are able to predict production curves with high confidence, but repetitive tasks in a few cases may spend a precious time of staff as well as require a large computational effort. Although artificial intelligence (AI) is providing an alternative path to the usual workflow, many commercial simulators lack robust AI algorithms. This work introduces a methodology based on a multilayer perceptron (MLP) neural network to predict the final cumulative oil production of a reservoir at vertical wells that cross hydraulic flow units (HFUs), which are volumes endowed with good flow attributes. Each well location is attached to special spots previously determined from clustering and calculation of maximum closeness centrality points (MaxCs) within a class of HFUs. The database is divided into training, validation, and testing sets organized after processing the UNISIM-I-D synthetic model, representative of the Namorado Field, Campos Basin, Brazil. The key rationale of this paper is to use the feature of MaxCs of being drivers for well placement as knowledge base to learn the production mechanisms of the oilfield. The outcomes are presented from two perspectives: an original MLP and its post-processed version. Both are compared with reservoir simulations carried out in CMG Imex $$^{\copyright }$$© and achieve reasonable agreement. The performance is measured by root-mean-squared error (RMSE) and mean absolute scaled error (MASE) both in original and post-processed versions. We show that average RMSE and MASE values near 0.07 and 14.00, respectively, are achieved without post-processing. With post-processing, gains of up to 43% are reported for the integral oil volume.

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.