No description available.

[pt] Na área de Exploração e Produção (EeP) de petróleo, uma das tarefas mais importantes é o estudo minucioso das características do reservatório para a criação de modelos de simulação que representem adequadamente as suas características. Durante a vida produtiva de um reservatório, o seu modelo de simulação correspondente precisa ser ajustado periodicamente, pois a disponibilidade de um modelo adequado é fundamental para a obtenção de previsões acertadas acerca da produção, e isto impacta diretamente a tomada de decisões gerenciais. O ajuste das propriedades do modelo se traduz em um problema de otimização complexo, onde a quantidade de variáveis envolvidas cresce com o aumento do número de blocos que compõem a malha do modelo de simulação, exigindo muito esforço por parte do especialista. A disponibilidade de uma ferramenta computacional, que possa auxiliar o especialista em parte deste processo, pode ser de grande utilidade tanto para a obtenção de respostas mais rápidas, quanto para a tomada de decisões mais acertadas. Diante disto, este trabalho combina inteligência computacional através de Algoritmo Genético Co-Evolutivo com Geoestatística de Múltiplos Pontos, propondo e implementando uma arquitetura de otimização aplicada ao ajuste de propriedades de modelos de reservatórios. Esta arquitetura diferencia-se das tradicionais abordagens por ser capaz de otimizar, simultaneamente, mais de uma propriedade do modelo de simulação de reservatório. Utilizou-se também, processamento distribuído para explorar o poder computacional paralelo dos algoritmos genéticos. A arquitetura mostrou-se capaz de gerar modelos que ajustam adequadamente as curvas de produção, preservando a consistência e a continuidade geológica do reservatório obtendo, respectivamente, 98 por cento e 97 por cento de redução no erro de ajuste aos dados históricos e de previsão. Para os mapas de porosidade e de permeabilidade, as reduções nos erros foram de 79 por cento e 84 por cento, respectivamente. / [en] In the Exploration and Production (EeP) of oil, one of the most important tasks is the detailed study of the characteristics of the reservoir for the creation of simulation models that adequately represent their characteristics. During the productive life of a reservoir, its corresponding simulation model needs to be adjusted periodically because the availability of an appropriate model is crucial to obtain accurate predictions about the production, and this directly impacts the management decisions. The adjustment of the properties of the model is translated into a complex optimization problem, where the number of variables involved increases with the increase of the number of blocks that make up the mesh of the simulation model, requiring too much effort on the part of a specialist. The availability of a computational tool that can assist the specialist on part of this process can be very useful both for obtaining quicker responses, as for making better decisions. Thus, this work combines computational intelligence through Coevolutionary Genetic Algorithm with Multipoint Geostatistics, proposing and implementing an architecture optimization applied to the tuning properties of reservoir models. This architecture differs from traditional approaches to be able to optimize simultaneously more than one property of the reservoir simulation model. We used also distributed processing to explore the parallel computing power of genetic algorithms. The architecture was capable of generating models that adequately fit the curves of production, preserving the consistency and continuity of the geological reservoir obtaining, respectively, 98 percent and 97 percent of reduction in error of fit to the historical data and forecasting. For porosity and permeability maps, the reductions in errors were 79 percent and 84 percent, respectively.

Orientador: Denis Jose Schiozer / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica e Instituto de Geociencias / Made available in DSpace on 2018-08-13T11:18:36Z (GMT). No. of bitstreams: 1 Risso_ValmirFrancisco_D.pdf: 9435021 bytes, checksum: 278e304092ceeb3f841e585ac7a34e55 (MD5) Previous issue date: 2007 / Resumo: O ajuste de histórico de produção tem como principal objetivo calibrar modelos numéricos de campos de petróleo para que os resultados de produção e de pressão da simulação sejam coerentes com o histórico de produção e de pressão observados e que estes modelos ajustados possam ser usados na previsão de produção com maior confiabilidade. Essa técnica apresenta algumas limitações, principalmente no início do desenvolvimento do campo quando há menos dados observados e as incertezas são maiores, o que torna o processo de ajuste do modelo numérico menos confiável. Com o avanço das técnicas de processamento sísmico e com a sísmica 4D, já é possível a obtenção de mapas de saturação do campo e com essa informação adicional, melhorar a qualidade do modelo em estudo possibilitando realizar previsões de comportamento do campo mais confiáveis, principalmente em campos onde a água proveniente de poços injetores ou de aqüíferos ainda não alcançou os poços produtores. O trabalho atual propõe uma metodologia para aumentar a confiabilidade do modelo numérico através da incorporação dos mapas de saturação no processo de ajuste do histórico do campo, combinando estas informações com os dados de produção de óleo, água e gás, de injeção e de pressão. A utilização dos mapas no processo de ajuste aumenta o número de parâmetros a serem analisados no processo de ajuste, aumentando assim o número de simulações necessárias e dificultando a análise dos resultados. Uma alternativa para tentar minimizar esse problema é a metodologia do planejamento estatístico e da superfície de resposta, a qual permite estudar um número maior de variáveis e regiões críticas ao mesmo tempo possibilitando otimizar ou minimizar várias respostas simultaneamente, estruturando melhor as etapas do processo de ajuste evitando-se o processo usual de tentativa e erro. / Abstract: The main objective of history matching is to improve numerical models of oil fields by incorporating observed data, production and pressure, into the characterization process, in order to obtain more reliable production forecasting. This technique presents some limitations mainly in the beginning of the development of oil fields, when less information is available and higher uncertainties are present. With seismic 4D, it is possible to obtain saturation maps allowing the improvement of the numerical model yielding a more reliable production forecasting. The objective of this work is to developed a methodology to improve the numerical model through the incorporation of the saturation maps in the process of history matching. The process requires a higher number of critical parameters to be analyzed in the adjustment process; therefore, it is necessary to increase the number of simulations yielding a more complex procedure. An alternative to minimize this problem is the statistical design and response surface methodologies which allow to study many variables and regions at the same time. It is possible to optimize some answers simultaneously, improving the process by reducing the manual work yielding better results. / Doutorado / Reservatórios e Gestão / Doutor em Ciências e Engenharia de Petróleo

Orientador: Denis Jose Schiozer / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecanica, Instituto de Geociencias / Made available in DSpace on 2018-08-12T14:20:01Z (GMT). No. of bitstreams: 1 Grecco_ConstantinoBornia_M.pdf: 11107825 bytes, checksum: a7e93880683d25011e16d6e8aaab8be3 (MD5) Previous issue date: 2008 / Resumo: A técnica de ajuste de histórico de produção consiste em modificar um modelo de simulação de reservatório para que ele fique consistente com os dados de produção, dentro das restrições observadas na fase de caracterização geológica. Essa técnica é limitada, principalmente em campos antigos, quando o histórico de produção não é muito confiável, ou no início de produção, quando há menos dados observados e as incertezas são maiores. O advento de novas tecnologias para obtenção de dados de saturação no decorrer da vida produtiva dos reservatórios, como é o caso da sísmica 4D e das ferramentas de perfilagem TDT/TDM, ajudou a superar algumas dificuldades da fase de construção do modelo geológico, mas o grande desafio tem sido em utilizar esses dados de maneira a beneficiar a produção de petróleo. Metodologias de ajuste de histórico utilizando dados de saturação da sísmica 4D já são encontrados na literatura, mas nenhum trabalho foi encontrado utilizando os dados de perfis. A vantagem dos dados de perfilagem é a maior precisão, mas, por outro lado, as informações são limitadas a alguns pés ao redor dos poços. O objetivo deste trabalho é integrar o processo tradicional de ajuste de histórico com dados de saturação de perfis, desenvolvendo modelos de simulação mais precisos, produzindo previsões de produção mais confiáveis e facilitando futuras tomadas de decisão. Os dados de saturação são utilizados como um novo parâmetro a ser ajustado e como uma ferramenta auxiliar para a definição das regiões críticas do reservatório, que serão alteradas. Uma metodologia de ajuste de histórico assistido utilizando dados de saturação, linhas de fluxo e um algoritmo de otimização é proposta e aplicada a um modelo sintético de reservatório. Parâmetros do processo são estudados e detalhados, achando a melhor maneira de usar os dados. O modelo também é ajustado sem o uso de informações de saturação e previsões dos modelos ajustados são comparadas, mostrando os benefícios e restrições da nova metodologia. / Abstract: In the production history matching process, the reservoir simulation model is modified in a way that it becomes consistent with production data, keeping the observed restrictions of the geological characterization phase. This technique is limited, mainly in mature fields, when the production history is not reliable, or in the beginning of production, when there are only a few observed data and uncertainties are higher. The development of new saturation data acquisition tools, such as 4D seismic and TDT/TDM logging tools helped to overcome some difficulties in the geologic model construction phase but the great challenge is how to use this data in a way to improve the petroleum production. History matching methodologies integrated with saturation data from 4D seismic are available in literature but no publications that utilize saturation data obtained from well logging were found. The advantage of the logging tools is the data accuracy but, on the other hand, it is limited to a few feet around the wells. The main objective of this project is to integrate the traditional history matching process with logging saturation data, developing more reliable simulation models and production forecasts and assisting future decision making processes. The saturation data is utilized as a new parameter to be matched as well as an auxiliary tool to help to determine critical regions which will be modified. An assisted history matching methodology utilizing saturation data, streamlines and an optimization algorithm is proposed and applied to a synthetic reservoir model. Parameters of the process are studied and detailed, finding the best way to use the data. The model is also history matched with no saturation information and predictions of the matched models are compared, showing the benefits and restrictions of the new methodology. / Mestrado / Reservatórios e Gestão / Mestre em Ciências e Engenharia de Petróleo

This thesis describes two novel Bayesian methods: the Iterative Ensemble Square Filter (IEnSRF) and the Warp Ensemble Square Root Filter (WEnSRF) for solving the barcode detection problem, the deconvolution problem in well testing and the history matching problem of facies patterns. For the barcode detection problem, at the expanse of overestimating the posterior uncertainty, the IEnSRF efficiently achieves successful detections with very challenging real barcode images which the other considered methods and commercial software fail to detect. It also performs reliable detection on low-resolution images under poor ambient light conditions. For the deconvolution problem in well testing, the IEnSRF is capable of quantifying estimation uncertainty, incorporating the cumulative production data and estimating the initial pressure, which were thought to be unachievable in the existing well testing literature. The estimation results for the considered real benchmark data using the IEnSRF significantly outperform the existing methods in the commercial software. The WEnSRF is utilised for solving the history matching problem of facies patterns. Through the warping transformation, the WEnSRF performs adjustment on the reservoir features directly and is thus superior in estimating the large-scale complicated facies patterns. It is able to provide accurate estimates of the reservoir properties robustly and efficiently with reasonably reliable prior reservoir structural information.

[pt] Os métodos baseados no filtro de Kalman têm tido sucesso notável na indústria do petróleo nos últimos anos, especialmente, para resolver problemas reais de ajuste de histórico. No entanto, como a formulação desses métodos é baseada em hipóteses de gaussianidade e linearidade, seu desempenho é severamente degradado quando a geologia a priori é descrita em termos de distribuições complexas (e.g. modelos de fácies). A tendência atual em soluções para o problema de ajuste de histórico é levar em consideração modelos de reservatórios mais realistas com geologia complexa. Assim, a modelagem de fácies geológicas desempenha um papel importante na caracterização de reservatórios, como forma de reproduzir padrões importantes de heterogeneidade e facilitar a modelagem das propriedades petrofísicas das rochas do reservatório. Esta tese introduz uma nova metodologia para realizar o ajuste de histórico de modelos geológicos complexos. A metodologia consiste na integração de métodos baseados no filtro de Kalman em particular o método conhecido na literatura como Ensemble Smoother with Multiple Data Assimilation (ES-MDA), com uma parametrização das fácies geológicas por meio de técnicas baseadas em aprendizado profundo (Deep Learning) em arquiteturas do tipo autoencoder. Um autoencoder sempre consiste em duas partes, o codificador (modelo de reconhecimento) e o decodificador (modelo gerador). O procedimento começa com o treinamento de um conjunto de realizações de fácies por meio de algoritmos de aprendizado profundo, através do qual são identificadas as principais características das imagens de fácies geológicas, permitindo criar novas realizações com as mesmas características da base de treinamento com uma reduzida parametrização dos modelos de fácies na saída do codificador. Essa parametrização é regularizada no codificador para fornecer uma distribuição gaussiana na saída, a qual é utilizada para atualizar os modelos de fácies de acordo com os dados observados do reservatório, através do método ES-MDA. Ao final, os modelos atualizados são reconstruídos através do aprendizado profundo (decodificador), com o objetivo de obter modelos finais que apresentem características similares às da base de treinamento. Os resultados, em três casos de estudo com 2 e 3 fácies, mostram que a parametrização de modelos de fácies baseada no aprendizado profundo consegue reconstruir os modelos de fácies com um erro inferior a 0,3 por cento. A metodologia proposta gera modelos geológicos ajustados que conservam a descrição geológica a priori do reservatório (fácies com canais curvilíneos), além de ser consistente com o ajuste dos dados observados do reservatório. / [en] Kalman filter-based methods have had remarkable success in the oil industry in recent years, especially to solve several real-life history matching problems. However, as the formulation of these methods is based on the assumptions of gaussianity and linearity, their performance is severely degraded when a priori geology is described in terms of complex distributions (e.g., facies models). The current trend in solutions for the history matching problem is to take into account more realistic reservoir models, with complex geology. Thus the geological facies modeling plays an important role in the characterization of reservoirs as a way of reproducing important patterns of heterogeneity and to facilitate the modeling of the reservoir rocks petrophysical properties. This thesis introduces a new methodology to perform the history matching of complex geological models. This methodology consists of the integration of Kalman filter-based methods, particularly the method known in the literature as Ensemble Smoother with Multiple Data Assimilation (ES-MDA), with a parameterization of the geological facies through techniques based on deep learning in autoencoder type architectures. An autoencoder always consists of two parts, the encoder (recognition model) and the decoder (generator model). The procedure begins with the training of a set of facies realizations via deep generative models, through which the main characteristics of geological facies images are identified, allowing for the creation of new realizations with the same characteristics of the training base, with a low dimention parametrization of the facies models at the output of the encoder. This parameterization is regularized at the encoder to provide Gaussian distribution models in the output, which is then used to update the models according to the observed data of the reservoir through the ES-MDA method. In the end, the updated models are reconstructed through deep learning (decoder), with the objective of obtaining final models that present characteristics similar to those of the training base. The results, in three case studies with 2 and 3 facies, show that the parameterization of facies models based on deep learning can reconstruct facies models with an error lower than 0.3 percent. The proposed methodology generates final geological models that preserve the a priori geological description of the reservoir (facies with curvilinear channels), besides being consistent with the adjustment of the observed data of the reservoir.

Modern reservoir management typically involves simulations of geological models to predict future recovery estimates, providing the economic assessment of different field development strategies. Integrating reservoir data is a vital step in developing reliable reservoir performance models. Currently, most effective strategies for traditional manual history matching commonly follow a structured approach with a sequence of adjustments from global to regional parameters, followed by local changes in model properties. In contrast, many of the recent automatic history matching methods utilize parameter sensitivities or gradients to directly update the fine-scale reservoir properties, often ignoring geological inconsistency. Therefore, there is need for combining elements of all of these scales in a seamless manner. We present a hierarchical streamline-assisted history matching, with a framework of global-local updates. A probabilistic approach, consisting of design of experiments, response surface methodology and the genetic algorithm, is used to understand the uncertainty in the large-scale static and dynamic parameters. This global update step is followed by a streamline-based model calibration for high resolution reservoir heterogeneity. This local update step assimilates dynamic production data. We apply the genetic global calibration to unconventional shale gas reservoir specifically we include stimulated reservoir volume as a constraint term in the data integration to improve history matching and reduce prediction uncertainty. We introduce a novel approach for efficiently computing well drainage volumes for shale gas wells with multistage fractures and fracture clusters, and we will filter stochastic shale gas reservoir models by comparing the computed drainage volume with the measured SRV within specified confidence limits. Finally, we demonstrate the value of integrating downhole temperature measurements as coarse-scale constraint during streamline-based history matching of dynamic production data. We first derive coarse-scale permeability trends in the reservoir from temperature data. The coarse information are then downscaled into fine scale permeability by sequential Gaussian simulation with block kriging, and updated by local-scale streamline-based history matching. he power and utility of our approaches have been demonstrated using both synthetic and field examples.

To meet the goal of atmospheric stabilization of carbon dioxide (CO2 ) levels a technological transformation should occur in the energy sector. One strategy to achieve this is carbon sequestration. Carbon dioxide can be captured from industrial sources and sequestered underground into depleted oil and gas reservoirs. CO2 injected into geological formations, such as mature oil reservoirs can be effectively trapped by hydrodynamical (structural), solution, residual (capillary) and mineral trapping methods. In this work, a case study was conducted using CMG-STARS software for CO2 sequestration in a mature oil field. History matching was done with the available production, bottom hole pressures and water cut data to compare the results obtained from the simulator with the field data. Next, previously developed optimization methods were modified and used for the case of study. The main object of the optimization was to determine the optimal location, number of injection wells, injection rate, injection depth and pressure of wells to maximize the total trapped amount of CO2 while enhancing the amount of oil recovered. A second round of simulations was carried out to study the factors that affect the total oil recovery and CO2 &not / storage amount. These include relative permeability end points effect, hysteresis effect, fracture spacing and additives of simultaneous injection of carbon dioxide with CO and H2S. Optimization runs were carried out on a mildly heterogeneous 3D model for variety of cases. When compared with the base case, the optimized case led to an increase of 20% in the amount of oil that is recovered / and more than 95% of the injected CO2 was trapped as solution gas on and as an immobile gas. Finally, an investigation of the economical feasibility was accomplished. NPV values for various cases were obtained, selected and studied yielding in a number of cases that are found to be applicable for the field of concern.

This thesis investigates methane emissions and methane production potentials from abandoned longwall panels produced or emitted due to mining activities either from coal seam or any underlying or overlying formations. These emissions can increase greenhouse gas concentrations and also pose a danger to the underground working environment and to miners. In addition to the safety issues, recovery and utilization of this gas is an additional source of energy. In this study, methane concentrations measured from ventilation air ways in Yeni &Ccedil / eltek Coal Mine, which is located in Suluova basin, Amasya, and contains thick, laterally extensive Lower Eocene coal seams, were integrated within a numerical vi reservoir model. Key reservoir parameters for history matching are cleat permeabilities, cleat porosity, diffusion time and Langmuir volume and Langmuir pressure. Thirteen cases were studied. According to the results, Case-10 determined as the best fitted case for both of the production wells. Cleat permeabilities and Langmuir pressure were the most effective parameters. Reservoir parameters matched are cleat permeabilities of 5, 4 and 1 md and fracture dimensions of 0.8, 0.4, and 0.1 m in x, y and z direction respectively, 2 % cleat porosity, 0.3 % water saturation. Diffusion time was determined as 400 days and 2000 kPa Langmuir volume and 6.24279 m3 /tone gas content estimated. According to these results it can be said that methane production will not be economically feasible, however / to remedy underground working conditions and safety of workers methane management should be taken into consideration.

“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.