Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

On-line learning of a fuzzy controller for a precise vehicle cruise control system

Usually, vehicle applications require the use of artificial intelligent techniques to implement control methods, due to noise provided by sensors or the impossibility of full knowledge about dynamics of the vehicle (engine state, wheel pressure or occupiers weight). This work presents a method to on-line evolve a fuzzy controller for commanding vehicles? pedals at low speeds; in this scenario, the slightest alteration in the vehicle or road conditions can vary controller?s behavior in a non predictable way. The proposal adapts singletons positions in real time, and trapezoids used to codify the input variables are modified according with historical data. Experimentation in both simulated and real vehicles are provided to show how fast and precise the method is, even compared with a human driver or using different vehicles

Recurrent Neural Network Based Control for Risers and Oil Wells

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.Redes Neurais Recorrentes tendem a ser custosas de se otimizar, porém possuem proprie- dades desejáveis para identificação de sistemas dinâmicos e servem como aproximadores universais dos mesmos. Para diminuir este custo considerado impraticável, surgiu na literatura as Redes de Estado de Echo (Echo State Networks). Echo State Networks são Redes Neurais Recorrentes divididas em duas partes: uma rede de neurônios reccorentes, chamada de reservatório, onde os pesos são fixos e inicializados aleatóriamente e uma camada composta de neurônios estáticos, utilizados para computar a saída do modelo de aprendizagem dinâmica. Somente os pesos de saída desta rede são treinados, podendo ser utilizados algoritmos do tipo mínimos quadrados. Devido a estas propriedades, tais redes podem aproximar sistemas dinâmicos complexos custando baixo esforço computa- tional, tendo obtido resultados promissores em aplicações de identificação e controle em malha fechada de sistemas dinâmicos. Há demonstrações promissoras do uso desse tipo de modelo em problemas envolvendo a indústria de petróleo e gás. Ao mesmo tempo, na industria de petróleo, várias abordagens são desenvolvidas para resolver o problem de golfadas utilizando controle em malha fechada. O problema de golfadas é pertinente numa plataforma de produção por ser capaz de causar grandes prejuizos na produção de petróleo, acarretando em perdas financeiras severas. Pensando nesta aplicação, este trabalho emprega uma estratégia de controle adaptativo utilizando Redes de Estado de Eco para se aproximar o modelo inverso do sistema controlado para o cálculo da ação de controle. Esta abordagem foi aplicada no controle da pressão de fundo de um poço de petróleo, juntamente com o controle anti-golfadas de um “riser”, cujo modelo estava submetido à um severo regime de golfadas. Para os experimentos, foram utilizados modelos já presentes em literatura para simulações. Testes de rejeição de perturbação e seguimento de referência foram aplicados no poço de petróleo. Para o riser, foi testado qual o ponto de equilíbrio estável com maior abertura do choke de produção que o riser consegue manter. Com base nos resultados obtidos, o presente trabalho demonstrou a aplicabilidade das Redes de Estado de Eco ao controle de plantas de produção da indústria de petróleo e gás e também demonstrou sua capacidade em efetuar a estabilização de regimes severos de golfadas.Recurrent Neural Networks (RNN) tend to be costly to optimize, though they posess desir- able properties for dynamic system identification and serve as an universal approximator for these systems. To diminish this cost which can make RNNs impracticable, Echo State Networks were proposed in literature. Echo State Networks (ESN) are Recurrent Neural Networks and are divided in two parts: a recurrent netwok, named reservoir, in which the weights involved are fixed and randomly initialized; and a readout layer, composed of static neurons, where the output of an Echo State Network is computed. Only the weights from the readout layer are trained. In this training, relatively low cost algorithms such as the least squares can be used. Due to these properties, ESN can approximate complex dynamical systems with relatively low computational effort and global minima guarantee, and has obtained promising results in system identification and closed loop control of dynamic systems. There are successful demonstrations of ESN application in oil and gas plants. At the same time, in oil industry, several approaches are developed to solve the slugging flow problem utilizing feedback control. slugging flow problems are pertinent in oil platforms due to being capable of hindering significantly oil production, implying severe financial loss. With this application in mind, this work uses an adaptive control utilizing ESN to approximate the controlled system’s inverse model to calculate the control action. This approach was applied to control the bottomhole pressure of an oil well and to apply anti-slug control of a pipeline-riser system which was subject to severe slugging flow regime. For the experiments, computer simulations were made utilizing models already stablished in literature. The closed-loop control of the oil well was subject to setpoint tracking and disturbance rejection tests. For the riser, it was tested which is the largest choke opening in which the riser maintains pressure stability, which corresponds to the maximum production without slugging flow. Based on the obtained results, this work demonstrated te applicability of ESN in oil production plants control and stabilization of severe slugging

Controle ativo de golfadas em poços de petróleo offshore

A produção de petróleo e gás é caracterizada pelo transporte dos fluidos do reservatório até as instalações de processamento, onde as correntes produzidas são tratadas e enquadradas de acordo com as especificações de comercialização, descarte ou reinjeção. A etapa de transporte dos fluidos até a planta de processamento é governada por complexos fenômenos de escoamento multifásico em longas tubulações, principalmente quando o ambiente de produção é marítimo. Esta combinação de cenários pode induzir o surgimento de padrões cíclicos de oscilação de pressão-vazão no escoamento do poço. Este fenômeno é classificado como um ciclo limite estável, que no estudo da dinâmica de sistemas é um comportamento não linear gerado por uma trajetória fechada no espaço de fase com formato de espiral quando o tempo tende ao infinito. Na indústria do petróleo, este ciclo limite é chamado de golfada, escoamento intermitente, slugging ou slug flow e é constituído pelo deslocamento de ondas de massa de fluido nas linhas de produção, o que coloca as instalações em risco e reduz a capacidade produtiva dos poços. Muitas publicações sobre métodos de controle deste fenômeno têm discutido o problema desde a década de 1980, contudo muitos pontos permanecem em aberto visto a complexidade e diversidade de cenários possíveis. Além disso, poucas aplicações em campo são reportadas na literatura, sendo que a maior parte dos trabalhos práticos publicados apresenta descrições limitadas que dificultam a replicação das metodologias utilizadas. Portanto, esta tese objetiva explorar abordagens de controle por retroalimentação (controle ativo) para problemas de ciclo limite em poços de petróleo em águas profundas e ultraprofundas. Aspectos como controle preditivo, multivariável e não linear são discutidos e explorados no trabalho, culminando em duas diferentes aplicações de campo descritas em detalhes. Até onde se sabe, esta é a primeira vez que estratégias de controle preditivo e de controle não linear são apresentadas na literatura em aplicações reais de controle ativo de golfadas. Como resultado, foi possível minimizar os efeitos adversos das golfadas e aumentar a produção dos poços em cerca de 10% nas aplicações reais.Oil and gas production is characterized by the transport of fluids from the reservoir to the processing facilities, where the streams produced are treated and fitted to commercial, disposal or reinjection specifications. The fluid transport stage to the processing plant is governed by complex multiphase flow phenomena in long pipelines, especially when the production environment is marine. This combination of scenarios can induce the appearance of singularities in the flow stability, resulting in the formation of cyclic flow patterns. This phenomenon is classified as a stable limit cycle, which in system dynamics means a nonlinear behavior generated by a closed trajectory in the phase space with a spiral shape when time tends to infinity. In the oil industry, this limit cycle is called slugging, slug flow or intermittent flow and causes pressure and flow waves in the well, exposing the facilities to risk and reducing production capacity. Several publications on methods of controlling this phenomenon have discussed the problem since the 1980s, however many points remain open due to the complexity and diversity of possible scenarios. Furthermore, few field applications are reported in the literature, and most of the published works present poor descriptions that make it hard to replicate the methodologies deployed. Therefore, this thesis aims to explore feedback control approaches (active control) for limit cycle problems in oil wells in deep and ultra-deepwaters environment. Aspects such as predictive, multivariable and nonlinear control are discussed and explored in this work, resulting in two different field applications described in detail. As far as is known, this is the very first time that predictive control and nonlinear control strategies are presented in the literature to deal with slugging in actual applications. As a result, it was possible to minimize the adverse effects of the slug flow and increase the production of the wells by about 10% in actual deployments

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

New multiphase flow measurements for slug control.

Severe slug flow is undesirable in offshore oil production systems, particularly for late-life fields. Active control through choking is one of the effective approaches to mitigating/controlling severe slug flow in oil production pipeline-riser systems. However, existing active slug control systems may limit oil production due to overchoking. Another problem in most active control systems is their dependency on information obtained from subsea measurements such as riser base pressure for active slug flow control. Both of these control challenges have been satisfactorily solved through the introduction of new multiphase flow topside measurements that are reliable and efficient in providing flow information for active slug control systems. By using Venturi multiphase flow topside measurements and Doppler ultrasonic measurements, an active slug flow control system is proposed to suppress severe slug flows without limiting oil production. Experimental and simulated results demonstrate that under active slug control, the proposed system is able not only to suppress slug flow but also to increase oil production compared to manual choking. Another objective of this research was to assess the applicability of continuous-wave Doppler ultrasonic (CWDU) techniques for accurate identification of gas-liquid flow regimes in pipeline-riser systems. Firstly, flow regime classification using the kernel multi-class support-vector machine (SVM) approach from machine learning (ML) was investigated. For a successful industrial application of this approach, the feasibility of conducting principal component analysis (PCA) for visualising the information from intrinsic flow regime features in two-dimensional space was also investigated. The classifier attained 84.6% accuracy on test samples and 85.7% accuracy on training samples. This approach showed the success of the CWDU, PCA-SVM, and virtual flow regime maps for objective two-phase flow regime classification on pipeline-riser systems, which would be possible for industrial application. Secondly, an approach that classifies the flow regime by means of a neural network operating on extracted features from the flow’s ultrasonic signals using either discrete wavelet transform (DWT) or power spectral density (PSD) was proposed. Using the PSD features, the neural network classifier misclassified 3 out of 31 test datasets and gave 90.3% accuracy, while only one dataset was misclassified with the DWT features, yielding an accuracy of 95.8%, thereby showing the superiority of the DWT in feature extraction of flow regime classification. This approach demonstrates the employment of a neural network and DWT for flow regime identification in industrial applications, using CWDU. The scheme has significant advantages over other techniques in that it uses a non-radioactive and non-intrusive sensor. The two investigated methods for gas-liquid two-phase flow regime identification appear to be the first known successful attempts to objectively identify gas-liquid flow regimes in an S-shape riser using CWDU. The CWDU approaches for flow regime classification on pipeline-riser systems were successful and proved possible in industrial applications.PhD in Energy and Powe

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Nonlinear Systems

Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems

Technology 2001: The Second National Technology Transfer Conference and Exposition, volume 2

Proceedings of the workshop are presented. The mission of the conference was to transfer advanced technologies developed by the Federal government, its contractors, and other high-tech organizations to U.S. industries for their use in developing new or improved products and processes. Volume two presents papers on the following topics: materials science, robotics, test and measurement, advanced manufacturing, artificial intelligence, biotechnology, electronics, and software engineering