The capacitance-resistance model (CRM) has been continuously improved and tested on both synthetic and real fields. For a large waterflood, with hundreds of injectors and producers present in a reservoir, tens of thousands of model parameters (gains, time constants, and productivity indices) in a field must be determined to completely define the CRM. In this case obtaining a unique solution in history-matching large reservoirs by nonlinear regression is difficult. Moreover, this approach is more likely to produce parameters that are statistically insignificant. The nonlinear nature of the CRM also makes it difficult to quantify the uncertainty in model parameters. The analytical solutions of the two linear reservoir models, the linearly transformed CRM whose control volume is the drainage volume around each producer (ltCRMP) and integrated capacitance-resistance model (ICRM), are developed in this work. Both models are derived from the governing differential equation of the producer-based representation of CRM (CRMP) that represents an in-situ material balance over the effective pore volume of a producer. The proposed methods use a constrained linear multivariate regression (LMR) to provide information about preferential permeability trends and fractures in a reservoir. The two models’ capabilities are validated with simulated data in several synthetic case studies. The ltCRMP and ICRM have the following advantages over the nonlinear waterflood model (CRMP): (1) convex objective functions, (2) elimination of the use of solver when constraints are ignored, and (3) faster computation time in optimization. In both methods, a unique solution can always be obtained regardless of the number of parameters as long as the number of data points is greater than the number of unknowns (parameters). The methods of establishing the confidence limits on CRMP gains and ICRM parameters are demonstrated in this work. This research also presents a method that uses the ICRM to estimate the gains between newly introduced injectors and existing producers for a homogeneous reservoir without having to do additional simulations or regression on newly simulated data. This procedure can guide geoscientists to decide where to drill new injectors to increase future oil recovery and provide rapid solutions without having to run reservoir simulations for each scenario. / text

The low salinity water injection technique (LSWI) has become one of the important research topics in the oil industry because of its possible advantages for improving oil recovery. Several mechanisms describing the LSWI process have been suggested in the literature; however, there is no consensus on a single main mechanism for the low salinity effect on oil recovery. As a result of the latter, there are few models for LSWI and especially for carbonates due to their heterogeneity and complexity. In this research, we proposed a systematic approach for modeling the LSWI effect on oil recovery from carbonates by proposing six different methods for history matching and three different LSWI models for the UTCHEM simulator, empirical, fundamental, and mechanistic LSWI models. The empirical LSWI model uses contact angle measurements and injected water salinity. The fundamental LSWI model captures the effect of LSWI through the trapping number. In the mechanistic LSWI model, we include the effect of different geochemical reactions through Gibbs free energy. Moreover, field-scale predictions of LSWI were performed and followed by a sensitivity analysis for the most influential design parameters using design of experiment (DoE). The LSWI technique was also optimized using the response surface methodology (RSM) where a response surface was built. Also, we moved a step further by investigating the combined effect of injecting low salinity water and carbon dioxide on oil recovery from carbonates through modeling of the process and numerical simulations using the UTCOMP simulator. The analysis showed that CO₂ is the main controller of the residual oil saturation whereas the low salinity water boosts the oil production rate by increasing the oil relative permeability through wettability alteration towards a more water-wet state. In addition, geochemical modeling of LSWI only and the combined effect of LSWI and CO₂ were performed using both UTCHEM and PHREEQC upon which the geochemical model in UTCHEM was modified and validated against PHREEQC. Based on the geochemical interpretation of the LSWI technique, we believe that wettability alteration is the main contributor to the LSWI effect on oil recovery from carbonates by anhydrite dissolution and surface charge change through pH exceeding the point of zero charge. / text

No description available.

The development of fast and efficient computer hardware technology has resulted in the rapid development of numerous computational software tools for making statistical inferences. The computational algorithms, which are the backbone of these tools, originate from distinct areas in science, mathematics and engineering. The main focus of this thesis is on computational tools which can be employed for estimating unmeasured variables in a process using all the available prior information. Specifically, this thesis demonstrates the application of a variety of tools for soft sensing of process variables and uncertain parameters of physiochemical process models, using routine data available from the process. The application examples presented in this thesis come from broad areas where process uncertainty is inherent and includes petrochemical processes, mechanical valve actuators, and upstream production processes in petroleum reservoirs. The mathematical models that are employed in different domains vary significantly in their structure and their level of complexity. In the petrochemical domain, the focus was on developing empirical soft sensors which are essentially nonparametric mathematical models identified using routine data from the process. The Support Vector Regression technique was applied for identifying such nonparametric empirical models. On the other hand, in all the other application examples in this thesis the physical parametric models of the process were utilized. The latter application examples, which cover a major portion of this thesis, demonstrate the application of modern state and parameter estimation algorithms which are firmly grounded on Bayesian theory and Monte Carlo techniques. Prior to the chapters on the application of state and parameter estimation techniques, a tutorial overview of the Monte Carlo simulation based state estimation algorithms is provided with an attempt to throw new light on these techniques. The tutorial is aimed at making these techniques simple to visualize and understand. The application case studies serve to illustrate the performance of the different algorithms. All case studies presented in this thesis are performed on processes that exhibit significant nonlinearity in terms of the relationship between the process input variables and output variables. / Process Control

Orientadores: Denis Jose Schiozer, Celio Maschio / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica e Instituto de Geociencias / Made available in DSpace on 2018-08-13T03:31:41Z (GMT). No. of bitstreams: 1 Sousa_SergioHenriqueGuerrade_M.pdf: 1592526 bytes, checksum: a308d06cf11fb891b71f7b3396952356 (MD5) Previous issue date: 2007 / Resumo: O problema do ajuste de histórico é uma das tarefas que mais demandam tempo em um estudo de reservatório baseado em simulações de fluxo, porque é um problema inverso onde os resultados (dados de produção) são conhecidos, porém os valores de entrada (a caracterização do reservatório) não são integralmente conhecidos. Adicionalmente, as funções objetivo que medem a qualidade do ajuste costumam ser expressões compostas por uma série de componentes que tornam a topologia do espaço de soluções complexa e repleta de não linearidades. A metodologia adotada neste trabalho foi a modelagem do problema de ajuste de histórico como um problema de otimização combinatória de modo que ele pudesse ser abordado através de processos metaheurísticos. Em particular, a metaheurística Busca Dispersa (Scatter Search) foi acoplada a um algoritmo de Busca Direta baseado no método de Hooke e Jeeves para resolver o problema do ajuste de histórico. Reservatórios sintéticos de solução conhecida foram utilizados para fazer a validação da metodologia e, em seguida, ela foi aplicada a outro reservatório, também sintético, mas com características de reservatórios reais onde a solução do ajuste é desconhecida. São discutidos ao longo do texto o uso da metodologia de forma automática e assistida e também os benefícios do uso da computação distribuída na execução do método. As maiores contribuições deste trabalho em relação à questão do ajuste de histórico são: a introdução de uma nova metodologia versátil para uso automático ou assistido, a discussão de algumas características que dificultam o processo de ajuste e de que forma eles podem ser contornados e também a abordagem do tema do ajuste automático vs. o ajuste assistido ilustrado com exemplos. / Abstract: The history matching problem is one of the most demanding tasks in a reservoir simulation study; because it's an inverse problem where the results (production data) are known but the input data (the reservoir characterization data) are not entirely known. Moreover, the objective function that guide the match is usually made out of a series of components that make the topology of the objective function both complex and full of non-linearities. The methodology adopted in this work was to model the history matching problem as a combinatorial optimization problem in order for it to be solved by metaheuristic processes. In particular, the Scatter Search metaheuristic was coupled with a direct search method based on Hooke and Jeeve's method to solve the history matching problem. Synthetic reservoirs of known solutions where used to validate the methodology and then the methodology was applied to another reservoir, also synthetic, but with characteristics of real reservoirs where the solution is not known in advance. Throughout the text, the mixed use of the methodology on both an assisted and automatic fashion is discussed along with the benefits attained by the use of distributed computing resources. The greatest contributions of this work related to the history matching problem are: the introduction of a new versatile methodology for both automatic and assisted matches, the discussion of some characteristics that burden the entire process and some ways to overcome the difficulties, and also the discussion of some tradeoffs between automatic versus assisted history matching with examples to illustrate the matter. / Mestrado / Reservatórios e Gestão / Mestre em Ciências e Engenharia de Petróleo

Orientadores: Denis Jose Schiozer, Celio Maschio / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica e Instituto de Geociencias / Made available in DSpace on 2018-08-12T07:35:35Z (GMT). No. of bitstreams: 1 Becerra_GustavoGabriel_M.pdf: 16760750 bytes, checksum: 0609c24d13d46b9121f71356ce9d42a1 (MD5) Previous issue date: 2007 / Resumo: A escassez de informações de qualidade introduz risco ao processo de previsão da produção de petróleo tornando imprescindível o ajuste de histórico de produção, que é a calibração do modelo a partir da resposta produtiva registrada. O ajuste é um problema inverso, em que diferentes combinações dos valores dos parâmetros do reservatório podem conduzir a respostas aceitáveis, especialmente quando o grau de incerteza desses parâmetros é elevado. A integração do ajuste de histórico com a análise probabilística dos cenários representativos conduz à obtenção de uma metodologia para detecção dos modelos calibrados dentro de uma faixa de aceitaçãodefinida. O tratamento de atributos interdependentes de influência global e local e o avanço por etapas são necessários. Desta forma, o objetivo deste trabalho é apresentar uma metodologia que integra a análise de incertezas com o ajuste de histórico em modelos de reservatórios complexos. Este procedimento auxilia a detectar os atributos incertos críticos e sua possível variação com o intuito de estimar a faixa representativa das reservas a desenvolver. Não é alvo obter o melhor ajuste determinístico, mas refletir como o histórico possibilita uma mitigação das incertezas. Assim, a meta é usar modelos mais complexos e aprimorar a metodologia iniciada por Moura Filho (2006), desenvolvida para um modelo teórico simples. São utilizados dois casos de estudo de complexidade similar. Um deles referente ao reservatório do Campo de Namorado, utilizado para verificar e validar, em nível global, a aplicação da metodologia. Na etapa de aplicação, é usado um modelo sintético construído a partir de dados de afloramentos reais no Brasil e compreendendo informações de campos análogos com sistemas turbidíticos depositados em águas profundas. Os métodos aplicados, mediante a redefinição das probabilidades associadas e níveis dos atributos incertos, permitem: (1) reduzir a faixa de ajustes possíveis e obter modelos mais confiáveis; (2) identificar e condicionar à incerteza presente em função dos dados registrados; (3) diminuir os intervalos de incerteza dos parâmetros críticos identificados; (4) demarcar os limites seguros do desempenho futuro do reservatório. A conseqüência é um aumento da confiança no uso da simulação como ferramenta auxiliar do processo decisório. Além disso, procura-se fornecer à equipe multidisciplinar uma metodologia para reduzir o tempo empregado no gerenciamento de múltiplos atributos incertos na etapa de ajuste do modelo. / Abstract: The lack of reliable data or with high degree of uncertainty yields risk to the process of production prediction making the history matching, the model calibration from the registered field production indispensable. History matching is an inverse problem and, in general, different combinations of reservoir attributes can lead acceptable solutions, especially whit high degree of uncertainty of these attributes. The integration of history matching with a probabilistic analysis of representative models yields a way to detect matched models inside an acceptance interval, providing more efficient framework for predictions. It is necessary to consider dependences between global and local attributes. The scope of this work is to present a methodology that integrates the uncertainty analysis with the history matching process in complex models. This procedure helps to detect critical subsurface attributes and their possible variation, in order to estimate a representative range of the additional reserves to be developed. . It is not an objective to obtain the best deterministic model, but to mitigate uncertainties by using observed data. The objective is to improve the methodology initiated by Moura Filho (2006), applied to a simple model. The methodology presented in this work is applied in two study cases with similar complexity. Firstly, the methodology is verified and validated, on global scale, in Namorado Field. Then, at the application stage, it is chosen a synthetic reservoir model made from real outcrop data of Brazil and involving information from analog fields with turbiditic systems deposited in deep waters. The methodology allows the redefinition of the probability and levels of the dynamic and static attributes in order: (1) to reduce the group of possible history matching obtaining more realistic models; (2) to identify the existent uncertainty as a function of observed data; (3) to decrease the uncertainty range of critical reservoir parameters; (4) to increase the confidence in production forecast. One contribution of this work is to present a quantitative approach to increase the reliability on the use of reservoir simulation as an auxiliary tool in decision processes. Another purpose of this work is to provide a procedure to reduce the consumed time to handle multiples uncertainty attributes during the history matching. / Mestrado / Reservatórios e Gestão / Mestre em Ciências e Engenharia de Petróleo

Submitted by isabela.moljf@hotmail.com (isabela.moljf@hotmail.com) on 2017-05-05T11:50:07Z No. of bitstreams: 1 carolinaribeiroxavier.pdf: 2823825 bytes, checksum: af5d50f5cdbb099ed71457b9baaabdc9 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-05-17T13:34:27Z (GMT) No. of bitstreams: 1 carolinaribeiroxavier.pdf: 2823825 bytes, checksum: af5d50f5cdbb099ed71457b9baaabdc9 (MD5) / Made available in DSpace on 2017-05-17T13:34:27Z (GMT). No. of bitstreams: 1 carolinaribeiroxavier.pdf: 2823825 bytes, checksum: af5d50f5cdbb099ed71457b9baaabdc9 (MD5) Previous issue date: 2009-08-18 / O processo de ajuste histórico tem como objetivo a determinação dos parâmetros de modelos de reservatório de petróleo. Uma vez ajustados, os modelos podem ser utilizados para a previsão do comportamento do reservatório. Este trabalho apresenta uma comparação de diferentes métodos de otimização para a solução deste problema. Métodos baseados em derivadas são comparados com um algoritmo genético. Em particular, compara-se os métodos: Levenberg-Marquardt, Quasi-Newton, Gradiente Conjugado n~ao linear, máxima descida e algoritmo genético. Devido à grande demanda computacional deste problema a computação paralela foi amplamente utilizada. As comparações entre os algoritmos de otimização foram realizadas em um ambiente de computação paralela heterogêneo e os resultados preliminares são apresentados e discutidos. / The process of history matching aims on the determination of the models' parameters from a petroleum reservoir. Once adjusted, the models can be used for the prediction of the reservoir behavior. This work presents a comparsion of different optimization methods for this problem's solution. Derivative based methods are compared to a genetic algorithm. In particular, the following methods are compared: Levenberg-Marquadt, Quasi-Newton, Non Linear Conjugate Gradient, steepest descent and genetic algorithm. Due to the great computational demand of this problem, the parallel computing has been widely used. The comparsions among the optimization algorithms were performed in an heterogeneous parallel computing environment and the preliminar results are presented and discussed.

Unlike the traditional two-stage methods, a conditional and inverse-conditional simulation approach may directly generate independent, identically distributed realizations to honor both static data and state data in one step. The Markov chain Monte Carlo (McMC) method was proved a powerful tool to perform such type of stochastic simulation. One of the main advantages of the McMC over the traditional sensitivity-based optimization methods to inverse problems is its power, flexibility and well-posedness in incorporating observation data from different sources. In this work, an improved version of the McMC method is presented to perform the stochastic simulation of reservoirs and aquifers in the framework of multi-Gaussian geostatistics. First, a blocking scheme is proposed to overcome the limitations of the classic single-component Metropolis-Hastings-type McMC. One of the main characteristics of the blocking McMC (BMcMC) scheme is that, depending on the inconsistence between the prior model and the reality, it can preserve the prior spatial structure and statistics as users specified. At the same time, it improves the mixing of the Markov chain and hence enhances the computational efficiency of the McMC. Furthermore, the exploration ability and the mixing speed of McMC are efficiently improved by coupling the multiscale proposals, i.e., the coupled multiscale McMC method. In order to make the BMcMC method capable of dealing with the high-dimensional cases, a multi-scale scheme is introduced to accelerate the computation of the likelihood which greatly improves the computational efficiency of the McMC due to the fact that most of the computational efforts are spent on the forward simulations. To this end, a flexible-grid full-tensor finite-difference simulator, which is widely compatible with the outputs from various upscaling subroutines, is developed to solve the flow equations and a constant-displacement random-walk particle-tracking method, which enhances the com / Fu, J. (2008). A markov chain monte carlo method for inverse stochastic modeling and uncertainty assessment [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1969 / Palancia

“History matching” (model calibration, parameter identification) is an established method for determination of representative reservoir properties such as permeability, porosity, relative permeability and fault transmissibility from a measured production history; however the uniqueness of selected model is always a challenge in a successful history matching. Up to now, the uniqueness of history matching results in practice can be assessed only after individual and technical experience and/or by repeating history matching with different reservoir models (different sets of parameters as the starting guess). The present study has been used the stochastical theory of Kullback & Leibler (K-L) and its further development by Akaike (AIC) for the first time to solve the uniqueness problem in reservoir engineering. In addition - based on the AIC principle and the principle of parsimony - a penalty term for OF has been empirically formulated regarding geoscientific and technical considerations. Finally a new formulation (Penalized Objective Function, POF) has been developed for model selection in reservoir history matching and has been tested successfully in a North German gas field. / „History Matching“ (Modell-Kalibrierung, Parameter Identifikation) ist eine bewährte Methode zur Bestimmung repräsentativer Reservoireigenschaften, wie Permeabilität, Porosität, relative Permeabilitätsfunktionen und Störungs-Transmissibilitäten aus einer gemessenen Produktionsgeschichte (history). Bis heute kann die Eindeutigkeit der identifizierten Parameter in der Praxis nicht konstruktiv nachgewiesen werden. Die Resultate eines History-Match können nur nach individueller Erfahrung und/oder durch vielmalige History-Match-Versuche mit verschiedenen Reservoirmodellen (verschiedenen Parametersätzen als Startposition) auf ihre Eindeutigkeit bewertet werden. Die vorliegende Studie hat die im Reservoir Engineering erstmals eingesetzte stochastische Theorie von Kullback & Leibler (K-L) und ihre Weiterentwicklung nach Akaike (AIC) als Basis für die Bewertung des Eindeutigkeitsproblems genutzt. Schließlich wurde das AIC-Prinzip als empirischer Strafterm aus geowissenschaftlichen und technischen Überlegungen formuliert. Der neu formulierte Strafterm (Penalized Objective Function, POF) wurde für das History Matching eines norddeutschen Erdgasfeldes erfolgreich getestet.

[pt] Simulação de reservatórios de óleo e gás é uma demanda comum em engenharia de petróleo e pesquisas relacionadas, que pode requerer um elevado custo computacional de tempo e processamento ao resolver um problema matemático. Além disso, alguns métodos de caracterização de reservatórios necessitam múltiplas iterações, resultando em muitas simulações para obter um resultado. Também podemos citar os métodos baseados em conjunto, tais como o ensemble Kalman filter, o EnKF, e o Ensemble Smoother With Multiple Data Assimilation,o ES-MDA, que requerem muitas simulações. Em contrapartida, o uso de aprendizado de máquina cresceu bastante na indústria de energia. Isto pode melhorar a acurácia de predição, otimizar estratégias e outros. Visando reduzir as complexidades de simulação de reservatórios, este trabalho investiga o uso de aprendizado de máquina como uma alternativa a simuladores convencionais. O modelo Random Forest Regressor é testado para reproduzir respostas de pressão em um reservatório multi-região radial composto. Uma solução analítica é utilizada para gerar o conjunto de treino e teste para o modelo. A partir de experimentação e análise, este trabalho tem o objetivo de suplementar a utilização de aprendizado de máquina na indústria de energia. / [en] Oil and gas reservoir simulation is a common demand in petroleum engineering, and research, which may have a high computational cost, solving a mathematical numeric problem, or high computational time. Moreover, several reservoir characterization methods require multiple iterations, resulting in many simulations to obtain a reasonable characterization. It is also possible to mention ensemble-based methods, such as the ensemble Kalman filter, EnKF, and the Ensemble Smoother With Multiple Data Assimilation, ES-MDA, which demand lots of simulation runs to provide the output result. As a result, reservoir simulation might be a complex subject to deal with when working with reservoir characterization. The use of machine learning has been increasing in the energy industry. It can improve the accuracy of reservoir predictions, optimize production strategies, and many other applications. The complexity and uncertainty of reservoir models pose significant challenges to traditional modeling approaches, making machine learning an attractive solution. Aiming to reduce reservoir simulation’s complexities, this work investigates using a machine-learning model as an alternative to conventional simulators. The Random Forest regressor model is experimented with to reproduce pressure response solutions for multi-region radial composite reservoirs. An analytical approach is employed to create the training dataset in the following procedure: the permeability is sorted using a specific distribution, and the output is generated using the analytical solution. Through experimentation and analysis, this work aims to advance our understanding of using machine learning in reservoir simulation for the energy industry.