Multiphase flow modelling for enhanced oil and gas drilling and production

From the exploration to the abandonment of an oil and gas discovery, operators and engineers are constantly faced with the challenge of achieving the best commercial potential of oil fields. Although the petroleum engineering community has significantly contributed towards maximising the potential of discovered prospects, the approach adopted so far has been compartmentalised with little (heuristics-based) or no quality integration. The highly interconnected nature of the decision factors affecting the management of any field requires increased implementation of Computer-Aided Process Engineering (CAPE) methods, thus presenting a task for which chemical engineers have the background to make useful contributions. Drilling and production are the two primary challenging operations of oilfield activities, which span through different time horizons with both fast and slow-paced dynamics. These attributes of these systems make the application of modelling, simulation, and optimisation tasks difficult. This PhD project aims to improve field planning and development decisions from a Process Systems Engineering (PSE) perspective via numerical (fluid dynamics) simulations and modelbased deterministic optimisation of drilling and production operations, respectively. Also demonstrated in this work is the importance of deterministic optimisation as a reliable alternative to classical heuristic methods. From a drilling operation perspective, this project focuses on the application of Computational Fluid Dynamics (CFD) as a tool to understand the intricacies of cuttings transport (during wellbore cleaning) with drilling fluids of non-Newtonian rheology. Simulations of two-phase solid-liquid flows in an annular domain are carried out, with a detailed analysis on the impact of several drilling parameters (drill pipe eccentricity, inclination angle, drill pipe rotation, bit penetration rate, fluid rheology, and particle properties) on the cuttings concentration, pressure drop profiles, axial fluid, and solid velocities. The influence of the flow regime (laminar and turbulent) on cuttings transport efficiency is also examined using the Eulerian-Eulerian and Lagrangian-Eulerian modelling methods. With experimentally validated simulations, this aspect of the PhD project provides new understanding on the interdependence of these parameters; thus facilitating industrial wellbore cleaning operations. The second part of this project applies mathematical optimisation techniques via reduced-order modelling strategies for the enhancement of petroleum recovery under complex constraints that characterise production operations. The motivation for this aspect of the project stems from the observation that previous PSE-based contributions aimed at enhancing field profitability, often apply over-simplifications of the actual process or neglect some key performance indices due to problem complexity. However, this project focuses on a more detailed computational integration and optimisation of the models describing the whole field development process from the reservoir to the surface facilities to ensure optimal field operations. Nonlinear Programs (NLPs), Mixed-Integer Linear Programs (MILPs), and Mixed-Integer Nonlinear Programs (MINLPs) are formulated for this purpose and solved using high-fidelity simulators and algorithms in open-source and commercial solvers. Compared to previous studies, more flow physics are incorporated and rapid computations obtained, thus enabling real-time decision support for enhanced production in the oil and gas industry

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Development of a predictive controller for domestic water heating systems

O aquecimento de água é uma peça fundamental no consumo de energia de um agregado familiar e os esquentadores de água a gás sem tanque (TGWHs) são largamente utilizados. Contudo, existem desafios de projeto e engenharia para desenvolver equipamentos mais eficientes, com menores emissões gasosas e que proporcionem um melhor conforto ao utilizador. Um dos inconvenientes destes dispositivos é a dificuldade em manter a estabilidade da temperatura da água quente na saída, quando ocorrem mudanças no caudal de água. Estas mudanças são, geralmente, inesperadas e não podem ser antecipadas pelo controlador geralmente usado nestes equipamentos, afetando altamente o conforto do utilizador. Além disso, existe um elevado desperdício de água associado ao longo tempo de resposta no arranque a frio. Assim, existe a necessidade de um controlador rápido e robusto que seja capaz de ultrapassar estas limitações. Neste sentido, a presente dissertação propõe o desenvolvimento de um controlador preditivo baseado em modelo (MPC) a ser implementado em dispositivos de aquecimento de água, com a finalidade de proporcionar o conforto térmico ao utilizador e a poupança de água associada ao tempo de espera no arranque a frio. Foram desenvolvidos em Matlab/Simulink cinco controladores de temperatura: Proporcional, PID, feedforward PID, MPC e MPC com função adaptativa. As simulações em ambiente simulado demonstraram que os controladores tradicionais não são adequados para o controlo de TGWHs. Além disso, as duas técnicas de controlo preditivo desenvolvidas mostraram melhor desempenho do que os métodos de controlo convencionais para todos os cenários considerados, nomeadamente, mudança de setpoint, arranque a frio e alterações no caudal de água. Seguidamente à análise em ambiente simulado, foram realizadas simulações em tempo real, nomeadamente, simulações com hardware-inloop, com vista a implementar o controlador preditivo clássico em hardware de baixo custo. Foi analisada a influência do tempo de amostragem, do horizonte de predição, do horizonte de controlo e do número de iterações requeridas para resolver o problema quadrático, em cada intervalo de controlo, tanto na resposta de temperatura como na quantidade de memória exigida. Encontrando um equilíbrio entre os parâmetros do MPC e a memória requerida foi possível implementar o algoritmo MPC, num microcontrolador de 32 bits e num microcontrolador de 8 bits. Para a implementação no Arduino MKR Zero (32 bits) foi obtido um índice de conforto 6,8% superior ao obtido para a técnica atualmente utilizada em esquentadores a gás e para a implementação no Arduino Mega 2560 (8 bits), um índice de conforto 4,2% superior. Relativamente ao tempo de espera no arranque a frio, o tempo de estabilização foi reduzido, em média, 3,65 segundos. Com a implementação do controlador preditivo, em hardware de baixo custo, capaz de responder às oscilações de temperatura, demonstrou-se que é possível alcançar o conforto térmico do utilizador e garantir a sua segurança. Além disso, é possível reduzir o seu impacto ambiental diminuindo os gastos de água e energia.Water heating is a major part of a household’s energy consumption and tankless gas water heaters (TGWHs) are commonly used. However, there are design and engineering challenges to develop equipment with more efficiency, lower emissions and which provide better user comfort. One of the drawbacks of these devices is the difficulty in maintaining hot water temperature stability when changes in water flow occur. These changes are usually unexpected and cannot be anticipated by the controller usually used in these devices, highly affecting the user’s comfort. Furthermore, there is a high wastage of water associated with the long response time at cold start. Therefore, there is a necessity for a fast and robust controller which can overcome these limitations. In this sense, this dissertation proposes the development of a model predictive controller (MPC) to be implemented in water heating devices, with the purpose of providing thermal comfort to the user and water savings associated with cold start waiting time. Five temperature controllers were developed in Matlab/Simulink: Proportional, PID, feedforward PID, MPC and adaptive MPC. Simulations in a simulated environment have shown that traditional controllers are not appropriate for controlling TGWHs. In addition, the two predictive control techniques developed showed better performance than conventional control methods for all scenarios considered, including, setpoint change, cold start, and changes in water flow rate. After the analysis in the simulated environment, real-time simulations were performed, in particular, hardware-in-loop simulations, in order to implement the classic predictive controller in low-cost hardware. The influence of the sample time, the prediction horizon, the control horizon, the number of iterations required to solve the quadratic problem, at each control interval, on both the temperature response and the amount of memory required was investigated. Finding a balance between the MPC parameters and the required memory it was possible to implement the MPC algorithm, on a 32-bit microcontroller and on a 8-bit microcontroller. For the implementation on the Arduino MKR Zero (32 bits) a 6.8% higher comfort index was obtained than for the technique currently used in gas water heaters and for the implementation on the Arduino Mega 2560 (8 bits), a 4.2% higher comfort index was achieved. With respect to the waiting time at cold start, the settling time was reduced by an average of 3.65 seconds. With the implementation of the predictive controller, in low-cost hardware, capable of responding to temperature oscillations, it has been shown that it is possible to achieve user thermal comfort and ensure their safety. Additionally, it is possible to reduce the environmental impact by decreasing water and energy consumption.Mestrado em Engenharia Mecânic