With the recent development of smart-well technology, the reservoir community now faces the challenge of developing robust and efficient techniques for reservoir characterization by means of data inversion. Unfortunately, classical history-matching methodologies do not possess computational efficiency and robustness needed to assimilate data measured almost in real time. Therefore, the reservoir community has started to explore techniques previously applied in other disciplines. Such is the case of the representer method, a variational data assimilation technique that was first applied in physical oceanography. The representer method is an efficient technique for solving linear inverse problems when a finite number of measurements are available. To the best of our knowledge, a general representer-based methodology for nonlinear inverse problems has not been fully developed. We fill this gap by presenting a novel implementation of the representer method applied to the nonlinear inverse problem of identifying petrophysical properties in reservoir models. Given production data from wells and prior knowledge of the petrophysical properties, the goal of our formulation is to find improved parameters so that the reservoir model prediction fits the data within some error given a priori. We first define an abstract framework for parameter identification in nonlinear reservoir models. Then, we propose an iterative representer-based scheme (IRBS) to find a solution of the inverse problem. Sufficient conditions for convergence of the proposed algorithm are established. We apply the IRBS to the estimation of absolute permeability in single-phase Darcy flow through porous media. Additionally, we study an extension of the IRBS with Karhunen-Loeve (IRBS-KL) expansions to address the identification of petrophysical properties subject to linear geological constraints. The IRBS-KL approach is compared with a standard variational technique for history matching. Furthermore, we apply the IRBS-KL to the identification of porosity, absolute and relative permeabilities given production data from an oil-water reservoir. The general derivation of the IRBS-KL is provided for a reservoir whose dynamics are modeled by slightly compressible immiscible displacement of two-phase flow through porous media. Finally, we present an ad-hoc sequential implementation of the IRBS-KL and compare its performance with the ensemble Kalman filter. / text

The data assimilation process of adjusting variables in a reservoir simulation model to honor observations of field data is known as history matching and has been extensively studied for few decades. However, limited success has been achieved due to the high complexity of the problem and the large computational effort required by the practical applications. An automatic history matching module based on the ensemble Kalman filter is developed and validated in this dissertation. The ensemble Kalman filter has three steps: initial sampling, forecasting through a reservoir simulator, and assimilation. The initial random sampling is improved by the singular value decomposition, which properly selects the ensemble members with less dependence. In this way, the same level of accuracy is achieved through a smaller ensemble size. Four different schemes for the assimilation step are investigated and direct inverse and square root approaches are recommended. A modified ensemble Kalman filter algorithm, which addresses the preference to the ensemble members through a nonequally weighting factor, is proposed. This weighted ensemble Kalman filter generates better production matches and recovery forecasting than those from the conventional ensemble Kalman filter. The proposed method also has faster convergence at the early time period of history matching. Another variant, the singular evolutive interpolated Kalman filter, is also applied. The resampling step in this method appears to improve the filter stability and help the filter to deliver rapid convergence both in model and data domains. This method and the ensemble Kalman filter are effective for history matching and forecasting uncertainty quantification. The independence of the ensemble members during the forecasting step allows the benefit of high-performance computing for the ensemble Kalman filter implementation during automatic history matching. Two-level computation is adopted; distributing ensemble members simultaneously while simulating each member in a parallel style. Such computation yields a significant speedup. The developed module is integrated with reservoir simulators UTCHEM, GEM and ECLIPSE, and has been implemented in the framework Integrated Reservoir Simulation Platform (IRSP). The successful applications to two and three-dimensional cases using blackoil and compositional reservoir cases demonstrate the efficiency of the developed automatic history matching module.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 1998. / Vita. Includes bibliographical references (leaves 216-221). Available also in a digital version from Dissertation Abstracts.

Thesis (M.S.)--West Virginia University, 2000. / Title from document title page. Document formatted into pages; contains ix, 176 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 64-65).

Thesis (M.S.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains xii, 82 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references (p. 80-82).

Thesis (MTech (Chemical Engineering))--Cape Peninsula University of Technology, 2016. / Due to the rising cost involve with bringing new fields on stream, of producing residual crude from matured fields, and the significant enhancement in oil recovery provided when compared to conventional water-flooding, increasing attention is being given to chemical flooding technologies. This is particular of interest in mature fields that had previously undergone water flooding. These methods entail injecting chemicals such as surfactant, alkali, and polymer often in mixture into reservoirs to improve oil recovery. In this study a sulfonated surfactant was produced from cheap waste vegetable oils and its performance was assessed in terms of thermal stability at reservoir conditions, adsorption on different reservoir materials, gas chromatography characterization and a limited interfacial tension measurement to evaluate its ability to improve the recovery of crude oil. Waste vegetable oils have great potential as a sustainable and low cost feedstock as well as its low toxicity.

Orientadores: Denis José Schiozer, Célio Maschio / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica e Instituto de Geociências / Made available in DSpace on 2018-08-18T20:59:19Z (GMT). No. of bitstreams: 1 Vasconcelos_DavidDennysonSousa_M.pdf: 1907776 bytes, checksum: f7eeb89b73385df9b024d60d9968b96e (MD5) Previous issue date: 2011 / Resumo: As incertezas geológicas influenciam diretamente a previsão de comportamento de reservatórios de petróleo, podendo, muitas vezes, tornar mais complexo o uso de ferramentas como simuladores de fluxo. A integração de técnicas de redução de incertezas e ajuste de histórico ganha um importante destaque neste processo, principalmente devido às limitações apresentadas pelas técnicas tradicionais de ajuste de histórico, sobretudo em campos com poucos dados de produção e maiores incertezas. O objetivo principal desse trabalho é obter um ajuste de histórico probabilístico a partir da redução das incertezas do modelo de reservatório. A proposta desse estudo é apresentar contribuições a uma metodologia existente, com o objetivo de possibilitar o tratamento de um elevado número de atributos incertos e aumentar a eficiência do processo. O método consiste em um procedimento dinâmico de calibração de propriedades do reservatório, utilizando dados observados e técnicas de amostragem. Os atributos considerados, discretizados em níveis de incertezas (com uma probabilidade associada), são submetidos a um processo de amostragem, com o método de Hipercubo Latino e, posteriormente combinados estatisticamente. Cada combinação entre níveis dos diferentes atributos resulta em um modelo de simulação e, após realizadas as simulações, novas probabilidades são estimadas, para cada nível, a partir de um procedimento que utiliza a diferença entre os dados observados e simulados, relativos a cada modelo. A qualidade do ajuste obtido pode ser avaliada a partir das curvas de incertezas, compostas por modelos representativos das probabilidades iniciais e finais de cada atributo e através dos indicadores propostos nesse trabalho, como variabilidade das probabilidades e afastamentos por poço. Os resultados obtidos indicam um método capaz de fornecer resultados confiáveis no processo de mitigação de incertezas, quando há dados de histórico disponíveis. O aumento na qualidade dos resultados com esse método, para as situações onde os atributos possuem mais níveis discretos que o convencional (normalmente são 3 níveis), depende do esforço computacional (em termos do número de simulações). Contudo, não há um aumento expressivo do número de simulações, como ocorre na técnica de árvore de derivação usada em trabalhos anteriores / Abstract: The geological uncertainties influence directly the prediction of reservoir behavior, making more complex the use of tools such as flow simulators. The integration between mitigation uncertainties techniques and history matching gains an important emphasis in this process, mainly due to the limitations presented by history matching traditional techniques, especially in areas with little observed data and greater uncertainties. The main objective of this work is to set a probabilistic history matching from the mitigation of reservoir uncertainty. The purpose of this study is to provide input to an existing methodology, in order to allow treatment of a large number of uncertain attributes and increase process efficiency. The method involves a dynamic procedure of global and local calibration of the geological model, using observed data and sampling techniques. The considered attributes, discretized into uncertainty levels (with an associated probability), are undergoing a sampling process, with Latin Hypercube method and then statistically combined. Each combination among levels of different attributes results in a complete simulation model, and after the simulations are performed, new probabilities are estimated for each level, from a procedure that uses the difference between observed and simulated data for each model. The quality of the history matching process can be evaluated from the uncertainty curves, composed of representative models of initial and final probabilities of each attribute, and using the indicators proposed in this work, as probabilities variability and the difference between observed and simulated data by well. The results obtained with this methodology indicate a tool capable of providing reliable results in the uncertainty mitigation process, when there is observed data available. The increase in quality of results with this method, for situations where the attributes has a number of discrete levels higher than the conventional technique (3 levels) depends on the computational effort (in terms of simulations number), but without the significant increase in the simulations number, as in the derivation tree technique used in previous works / Mestrado / Reservatórios e Gestão / Mestre em Ciências e Engenharia de Petróleo

Orientador: Denis José Schiozer / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-19T21:21:13Z (GMT). No. of bitstreams: 1 Silva_LucianadosSantos_M.pdf: 12675920 bytes, checksum: 63532a17aa12aa538936a8b7e2f0b435 (MD5) Previous issue date: 2011 / Resumo: A grande quantidade de incertezas presente na modelagem de reservatórios gera riscos na previsão de comportamento de um campo de petróleo. Assim, torna-se imprescindível o ajuste de histórico, que é a calibração do modelo de simulação do reservatório com os dados dinâmicos observados, aproximando o modelo da realidade e gerando previsões mais confiáveis. Diversas metodologias surgiram para integrar a análise de incertezas com o ajuste de histórico, mas devido à complexidade do processo, algumas delas só se aplicam a casos simples. A proposta deste trabalho é aplicar os métodos desenvolvidos por Moura Filho (2006), Becerra (2007) e Maschio et al. (2010) em um caso complexo sintético, similar a um modelo real de um reservatório de petróleo e avaliá-los para propor melhorias na metodologia. A técnica consiste em utilizar as diferenças entre os dados de produção observados e os simulados para reduzir as incertezas do reservatório, calculando as probabilidades dos níveis dos parâmetros incertos. Para isso, os atributos incertos são discretizados em três níveis e é feita uma análise de sensibilidade para escolher os atributos críticos, os quais são combinados através da árvore de derivação para gerar os diferentes modelos de simulação. Com os dados históricos (medidos) e simulados destes modelos, é feita a redistribuição das probabilidades dos níveis utilizando e comparando dois métodos: o de Moura Filho e Becerra (Método 1) e o de Maschio et al. (Método 2). Os resultados deles não mostraram boa eficiência na redução das incertezas para o caso estudado, pois as curvas continuaram muito espalhadas com relação ao histórico. Sendo assim, foram criados o Método 3, utilizando as melhores práticas da formulação dos dois estudados com o objetivo de tornar a metodologia mais robusta para uso em casos reais, e o Método 4, que é uma reaplicação do Método 3 após a redefinição dos valores dos níveis dos atributos. Uma comparação dos resultados dos quatro métodos mostra a evolução da redução das incertezas. Além disso, consegue-se diminuir a dispersão dos modelos representativos, centralizando-os com relação ao histórico de produção, o que permite uma melhor previsão de produção e maior confiabilidade na análise de risco de projetos futuros / Abstract: The large amount of uncertainties in reservoir modeling increases petroleum production forecast risks. Therefore, the history matching, which refines the simulation model to closely reproduce production data, is a vital procedure once it approximates numerical models to reality providing reliable predictions. Many methodologies were developed to integrate uncertainty analysis and history matching in order to mitigate the reservoir uncertainties by using the observed data, but due to the process complexity, some of them are applicable only in simple cases. In this context, the present work aims to evaluate the application of existing methods, developed by Moura Filho (2006), Becerra (2007) and Maschio et al. (2010), in a synthetic complex model (i.e. similar to a real field) and propose new methods with some improvements to be applied in real cases of the petroleum industry. The main characteristic of these methods is the use of differences between observed and simulated data to recalculate the probabilities distribution of uncertain parameters with the purpose of reducing reservoir uncertainties. To apply the methods, the uncertainty attributes are discretized in three levels and a sensibility analysis is done to select the critical attributes, which are combined by a derivative tree generating different simulation models. With history and simulated data of these models, the redistribution of occurrence probabilities is made with different formulas: Moura Filho e Becerra (Method 1) and Maschio et al. (Method 2). These two methods are compared and their results don't show good efficiency in uncertainty reduction of the studied case, because the final curves remain widely scattered around history data. Then, two methods are proposed, Method 3, which combines the best practices of the two reviewed ones, making it robust to be used in real cases with a great number of wells and production functions to be adjusted such as water production and pressure. The second proposed one, Method 4, is a reapplication of the third method with a redefinition of attribute values in order to refine the results. A comparison of the results of the four methods shows an evolution in the uncertainty reduction. Besides that, there is a decrease in the dispersion of the representative curves, which are centralized around the history data, providing a better production forecast and greater reliability in risk analysis of future projects / Mestrado / Reservatórios e Gestão / Mestre em Ciências e Engenharia de Petróleo

A significant percentage of computational time in compositional simulations is spent performing flash calculations to determine the equilibrium compositions of hydrocarbon phases in situ. Flash calculations must be done at each time step for each grid block; thus billions of such calculations are possible. It would be very important to reduce the computational time of flash calculations significantly so that more grid blocks or components may be used. In this dissertation, three different methods are developed that yield fast, robust and accurate phase behavior calculations useful for compositional simulation and other applications. The first approach is to express the mixing rule in equations-of-state (EOS) so that a flash calculation is at most a function of six variables, often referred to as reduced parameters, regardless of the number of pseudocomponents. This is done without sacrificing accuracy and with improved robustness compared with the conventional method. This approach is extended for flash calculations with three or more phases. The reduced method is also derived for use in stability analysis, yielding significant speedup. The second approach improves flash calculations when K-values are assumed constant. We developed a new continuous objective function with improved linearity and specified a small window in which the equilibrium compositions must lie. The calculation speed and robustness of the constant K-value flash are significantly improved. This new approach replaces the Rachford-Rice procedure that is embedded in the conventional flash calculations. In the last approach, a limited compositional model for ternary systems is developed using a novel transformation method. In this method, all tie lines in ternary systems are first transformed to a new compositional space where all tie lines are made parallel. The binodal curves in the transformed space are regressed with any accurate function. Equilibrium phase behavior calculations are then done in this transformed space non-iteratively. The compositions in the transformed space are translated back to the actual compositional space. The new method is very fast and robust because no iteration is required and thus always converges even at the critical point because it is a direct method. The implementation of some of these approaches into compositional simulators, for example UTCOMP or GPAS, shows that they are faster than conventional flash calculations, without sacrificing simulation accuracy. For example, the implementation of the transformation method into UTCOMP shows that the new method is more than ten times faster than conventional flash calculations.