An Echo State Network-based Soft Sensor of Downhole Pressure for a Gas-lift Oil Well

Soft sensor technology has been increasingly used in indus- try. Its importance is magnified when the process variable to be estimated is key to control and monitoring processes and the respective sensor ei- ther has a high probability of failure or is unreliable due to harsh environ- ment conditions. This is the case for permanent downhole gauge (PDG) sensors in the oil and gas industry, which measure pressure and tempera- ture in deepwater oil wells. In this paper, historical data obtained from an actual offshore oil well is used to build a black box model that estimates the PDG downhole pressure from platform variables, using Echo State Networks (ESNs), which are a class of recurrent networks with power- ful modeling capabilities. These networks, differently from other neural networks models used by most soft sensors in literature, can model the nonlinear dynamical properties present in the noisy real-world data by using a two-layer structure with efficient training: a recurrent nonlinear layer with fixed randomly generated weights and a linear adaptive read- out output layer. Experimental results show that ESNs are a promising technique to model soft sensors in an industrial setting

Desenvolvimento de analisador virtual para predição da pressão de fundo em poços de petróleo utilizando rede neural

Na exploração de petróleo a melhor variável para estabilização do fluxo via golfadas é a Pressão no chamado de Downhole Pressure Gauge (Ppdg). Porém este instrumento é instalado em ambiente agressivo e, portanto tende a estragar facilmente. Além disso, devido sua localização a manutenção/substituição é normalmente inviável economicamente. Sendo assim, uma solução para garantir esta medida é utilizar como sensor um analisador virtual. Nesse contexto, utilizando de um modelo de rede neural feedforward do tipo caixa preta com dados do modelo dinâmico semi-empírico simplificado FOWM (Fast Offshore Wells Model), o objetivo do presente trabalho é desenvolver um analisador virtual capaz de predizer a Ppdg com o mínimo erro associado. Obteve-se como resultado otimizado o modelo com os hiperparâmetros: otimizador = Adadelta, inicialização = glorot_normal, ativação = relu, N° neurônios na 1° camada = 256 e N° neurônios na 2° camada = 0, sendo o coeficiente de determinação (R2) igual a 0.9998969 e um MSE de 5,02 X 10-6. Indicando assim, que o mesmo é capaz de substituir em uma planta real o sensor físico. Além disso, pode-se observar que um modelo do tipo caixa preta exige que a base de dados contemple a maioria dos cenários que a planta está sujeita a operar, tendo em vista que a capacidade preditiva extrapolava do modelo foi muito inferior a capacidade interpolativa, sendo o coeficiente de determinação da extrapolação inicial igual a 0,659041 e final igual a 0,699827. Por fim, quando testada a mesma base de dados no método clássico de predição, a regressão linear, obteve-se como R2 = 0,995022 e o MSE = 2X10-4. Indicando assim, que o método de rede neural é mais acurado para modelagem preditiva

Estimação de parâmetros em tempo real através de filtro de Kalman com janela robusta suavizante e estimadores de estados não lineares

Os estimadores de estado, ou observadores, são técnicas que reconstroem os estados de um modelo dinâmico a partir das medidas de entrada e saída do sistema. Eles podem ser baseados na teoria probabilística (proposto por Kalman), que considera ruídos no modelo ou na teoria determinística (introduzida por Luenberger) sem a presença de ruídos. Embora, na sua gênese, o controle “moderno” tenha motivado o surgimento dessas técnicas em 1960, os estimadores de estado são hoje em dia aplicados também em reconciliação de dados, analisadores virtuais, estimação de parâmetros, gêmeos digitais e detecção de falhas. Por isso, esta tese aborda um estudo sobre filtros de Kalman e suas aplicações focado, principalmente, no uso de janela robusta suavizante. As principais contribuições do trabalho são: (1) revisão bibliográfica histórica dos estimadores de estado, abordando suas principais interligações e características, incluindo uma motivação prática de suas utilizações; (2) avaliação de cinco metodologias de filtro de Kalman (estendido - EKF, estendido com restrições - CEKF, formulação curta do estendido com restrições – CEKF2, estendido com restrições e suavizado - CEKFS, sem rastro - UKF, e de cubatura – CKF implementadas a dados industriais, mostrando a sua capacidade de aplicação em casos reais, sendo eles, na produção de petróleo offshore e em uma rede de trocadores de calor; (3) proposta de técnica de estimação de bias em casos em que o estimador não linear retorna resultados insatisfatórios; (4) avaliação de três métodos de estimadores de estado com horizonte móvel para estimação simultânea de estados e parâmetros (estimação do horizonte móvel - MHE, com horizonte retrocedido - RNK, e robusto com horizonte retrocedido - RRNK); e (5) apresentação de formulação robusta e simples para problema de otimização do RNK e RRNK utilizando programação quadrática. De modo geral os filtros de Kalman não-lineares (UKF e CKF) retornam melhores resultados para os dados industriais quando o modelo está bem ajustado. No entanto, eles possuem elevado custo computacional e desempenho insatisfatório para modelos mal ajustados, enquanto os filtros estendidos não apresentam essas desvantagens. Por isso, utilizando técnica simples da estimação de bias como uma variável através de técnica de estado aumentado, o filtro de Kalman sem rastro e de cubatura se mostraram mais acurados, mesmo em um cenário de ajuste inadequado do modelo. Para a estimação simultânea de estados e parâmetros, o RRNK exibiu as suas vantagens na redução de erros de modelagem, retornando parâmetros mais suavizados. Nesse sentido, a reformulação dos problemas de otimização do RNK e RRNK em uma formulação de programação quadrática simples e robusta obteve um custo computacional nove vezes menor que o MHE.State estimators, or observers, are techniques that reconstruct the states of a dynamical model from the input and output measures of the system. They can be based on the probabilistic theory (proposed by Kalman), which considers noise in the model, or on the deterministic theory (introduced by Luenberger) without the presence of noise. Although in its genesis, “modern” control motivated the emergence of these techniques in 1960, state estimators are nowadays also applied in data reconciliation, virtual analyzers, parameter estimation, digital twins, and fault detection. For this reason, this thesis addresses a study on Kalman filters and their applications, focused mainly on the use of a robust softening window. The main contributions of the work are: (1) historical bibliographic review of state estimators, addressing their main interconnections and characteristics, including a practical motivation for their uses; (2) evaluation of five Kalman filter methodologies (extended – EKF, constrained extended – CEKF, short formulation of the constrained extended – CEKF2, constrained extended and smoother – CEKFS, unscented – UKF, cubature – CKF) implemented to industrial data, showing their ability to be applied in real cases, namely in offshore oil production and in a heat exchanger network; (3) proposal of bias estimation technique in cases where the nonlinear estimator returns unsatisfactory results; (4) evaluation of three methods of state estimators with moving window for simultaneous state and parameter estimation (moving horizon horizon – MHE, receding nonlinear Kalman filter – RNK, and robust receding nonlinear Kalman filter – RRNK), and (5) presentation of robust and simple formulation for RNK and RRNK optimization problem using quadratic programming. In general, non-linear Kalman filters (UKF and CKF) return better results for industrial data when the model is well adjusted. However, they have high computational costs and poor performance for poorly adjusted models, while extended filters do not present these disadvantages. Therefore, using a simple bias estimation technique as a variable using an increased state technique, the unscented and cubature Kalman filter proved to be more accurate, even in a scenario of inadequate model adjustment. For the simultaneous state and parameter estimation, the RRNK showed its advantages in reducing modeling errors, returning more smoothed parameters. In this sense, the RNK and RRNK optimization problems’ reformulation in a robust and straightforwards quadratic programming formulation obtained a computational cost nine times smaller than the MHE

Applying modern logging for minimize production risks in oil and gas wells

Clearly, everyone who works in oil and gas field knows that logging operations are very important to produce our oil and gas without any risks and undesirable incidents. There are many different purposes to use logging in our well. For instance, one of them is to find out our production point in oil and gas well. After the logging operation, the report paper of logging operation shows us where our resource is located. According to this, we can decide how many meters our well deep. Other reason using log is to determine curing time of cement. After running casing, immediately, cementing engineers come to the field to pump the cement to free space between the casings or casing and wellbore. These are different operations that’s why we use different kind of the log to analyze. Therefore, cement logging also is important. In this thesis, we are going to analyze and observe real cases and results of the logging operations in oil and gas fields. Mainly, in production zones logging operations should be done because undesirable incidents and risks are more than other zones. Currently, we use the most modern logging in our fields to ensure that everything is okay, and we can continue other operations. Production zone which is perforated is more dangerous zone because perforated zones may be banned, and it may cause well lost. As a result, production engineers lost their production zone, company is out of pocket and loss well structure. Learning about logs and logging operations and how to use with them helps to improve technical operations, also we don’t waste time with undesirable accidents, safety factor increases in the field. The most main thing in the oil and gas field is safety. Improving our capability and knowledge about using logs to maximize safety in the platform

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

Echo State Networks for Virtual Flow Metering in Gravity Separator

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.Echo State Network (ESN) é um tipo de Rede Neural Recorrente (Recurrent Neural Network) e pode ser usada para modelar certas classes de sistemas dinâmicos não lineares. O ESN contém uma grande rede neural recorrente com pesos fixos definidos aleatoriamente, que são chamados de reservatórios. Sob a influência de sinais de entrada (u(t)), o reservatório é uma coleção de alta dimensão de transformações não lineares (xi(t)) com a ativação da função de entrada (u(t)), do qual um sinal de saída desejado (y(t)) pode ser combinado. RNNs tende a ter um alto custo computacional para otimizar enquanto ESNs têm um baixo custo, isso porque apenas a camada de saída é treinada e os métodos populares, como least square, são computacionalmente efetivos. Virtual Flow Metering (VFM) é um método para estimar a frequência de fluxo para fases diferentes de um fluxo multifase sem medi-la diretamente, ao invés disso, ele faz uso de dados relacionados ao fluxo. Esse é um tópico de pesquisa na industria de gás e óleo, o qual é difícil de medir e modelar um fluxo de três fases de um poço que consiste de óleo, gás e água. Informações sobre a composição da corrente do poço podem potencialmente ser usada para planejar melhor a produção, para aumentar a redundância e a segurança e reduzir as interrupções na produção para medir a composição da corrente do poço submetendo-a a um teste de tanque. Este trabalho utiliza ESN para estimar um fluxo multifásico entrando em um tanque de separação trifásico por gravidade. A entrada do ESN inclui medidas de nível de água, de óleo e pressão, bem como os dados sobre o fluxo de saída das variáveis de controle correspondente à fase da água, do óleo e do gás no tanque. simulações foram utilizadas para representar o tanque. Nesse trabalho, ESN mostrou-se capaz de recriar uma corrente de poço tanto com águas com nível variável quanto com nível estático no tanque e também rejeitar ruído nos estados. Os resultados observados mostraram-se melhores daqueles baseados em filtros de Kalman extendidos.Echo State Networks (ESN) are a type of Recurrent Neural Networks (RNN) and can be used to model certain classes of nonlinear dynamical systems. The ESN contains a large recurrent neural network with fixed weights that are defined at random, which are called the reservoir. Under the influence of input signals (u(t)) the reservoir is a high-dimensional collection of nonlinearly transformed versions (xi(t)) with the activation function of the input (u(t)), from which a desired output signal (y(t)) can be combined. RNNs tends to be costly to optimize when ESNs have a low computational cost. This is because only the output layer is trained, and the popular methods, like least square, are computationally effective. Virtual Flow Metering (VFM) is a method for estimating flowrates for different phases of a multi-phase flow without measuring them directly. Instead it makes use of data related to the flow. VFM is a topic of research in the oil and gas industry, where it is difficult to measure and model the three-phase flow from a well consisting of oil, water and gas. A common approach in the industry is to obtain multi-phase measurements is to allocate the wellstream into a test tank. Information about the composition of the well stream could potentially be used to better plan the production, improve redundancy and safety and reduce interruptions in the production. This work uses ESN to estimate a multi-phase wellstream entering a three-phase gravity separation tank. The input to the ESN includes measurements of water level, liquid level and pressure, as well as the data on the control variables outflow from the water, oil and gas phase of the tank. For the experiments, simulations were used to represent the tank. In this work, ESN was shown able to recreate wellstream with both a stationery and varying water level in the tank and also rejecting noise on states. In this work ESN showed better results than an observer based on Extended Kalman Filter

Subsea Communication - Implementing and Evaluating Protocols

This thesis compares the performance, features, and practical implementation experience for subsea relevant protocols. Modbus TCP and Ethernet/IP are examples on mature communication protocols that can be used to exchange process data between topside and subsea. OPC UA is added as a suggestion to perform prospective data exchange between topside and subsea. The theoretical evaluation is performed for Modbus TCP, Ethernet/IP, and OPC UA. The evaluation reveals that Ethernet/IP is the only protocol that quantifies achievable performance indicators, Modbus TCP and OPC UA are not supposed to have performance indicators due its wide range of application. Also, the direct comparison of Modbus TCP and Ethernet/IP concludes that Ethernet/IP has better real-time properties, better multicast performance, and better support for features like time synchronization and safety protocol. CAN bus (SIIS level II) and IWIS are used for data exchange at sensor level, an introduction and evaluation of these two principles are performed. However, a direct comparison of the two were not considered relevant as they are engineered for two different purposes. The practical part of the thesis has consisted of implementing two different communication protocols that can be used for the same purpose. The first task was reading and interpreting the official Modbus TCP specification and implementing this for message exchange between two microcontrollers. The second task involved implementation of OPC UA on a microcomputer by use of a software development kit

Introduction to Permanent Plug and Abandonment of Wells

This open access book offers a timely guide to challenges and current practices to permanently plug and abandon hydrocarbon wells. With a focus on offshore North Sea, it analyzes the process of plug and abandonment of hydrocarbon wells through the establishment of permanent well barriers. It provides the reader with extensive knowledge on the type of barriers, their functioning and verification. It then discusses plug and abandonment methodologies, analyzing different types of permanent plugging materials. Last, it describes some tests for verifying the integrity and functionality of installed permanent barriers. The book offers a comprehensive reference guide to well plugging and abandonment (P&A) and well integrity testing. The book also presents new technologies that have been proposed to be used in plugging and abandoning of wells, which might be game-changing technologies, but they are still in laboratory or testing level. Given its scope, it addresses students and researchers in both academia and industry. It also provides information for engineers who work in petroleum industry and should be familiarized with P&A of hydrocarbon wells to reduce the time of P&A by considering it during well planning and construction