O principal objetivo deste trabalho foi avaliar um procedimento de campo para testar o comprimento mínimo que fraturas devem ter para construir uma rede de fluxo em escala de dezenas de metros ao longo de maciços cristalinos. O comprimento mínimo é determinado a partir do limite de percolação, definido por modelos baseados na Teoria da Percolação e a densidade de fraturas no poço, determinada pela perfilagem ótica. Para testar o valor de 3,9 metros que foi encontrado, foram realizados bombeamentos em dois poços próximos enquanto o nível estático era registrado no poço de observação. O método apresenta facilidades em termos de operação e para obter-se o número de fraturas que interceptam o poço, no entanto o registro do nível estático é afetado por fatores externos, como presença de efeitos de maré e variação da pressão atmosférica, e correções são necessárias para permitir identificar a interferência proveniente somente do bombeamento. / The objective of this work is to develop a field procedure to determine a minimum characteristic length forming a connected fracture network in crystalline rocks. This minimum length is determined as a percolation threshold defined by models based on Percolation Theory and fracture density data determined from borehole imaging with well-logging probes. The characteristic length (3.9 meters) once estimated for a testing well was evaluated by monitoring its water head meanwhile nearby wells at different distances were pumped. The water head variation recorded in the testing well was disturbed by interfering effects associated to earth tides and atmospheric pressure, requiring further corrections to isolate effects induced by pumping. The results of these tests validate the percolation limit determined from logging data suggesting a rock mass with very low connectivity. The proposed test can be regarded as simple and easy to apply in many practical situations, for example when evaluating groundwater resources or geotechnical properties in fractured crystalline rocks.

O principal objetivo deste trabalho foi avaliar um procedimento de campo para testar o comprimento mínimo que fraturas devem ter para construir uma rede de fluxo em escala de dezenas de metros ao longo de maciços cristalinos. O comprimento mínimo é determinado a partir do limite de percolação, definido por modelos baseados na Teoria da Percolação e a densidade de fraturas no poço, determinada pela perfilagem ótica. Para testar o valor de 3,9 metros que foi encontrado, foram realizados bombeamentos em dois poços próximos enquanto o nível estático era registrado no poço de observação. O método apresenta facilidades em termos de operação e para obter-se o número de fraturas que interceptam o poço, no entanto o registro do nível estático é afetado por fatores externos, como presença de efeitos de maré e variação da pressão atmosférica, e correções são necessárias para permitir identificar a interferência proveniente somente do bombeamento. / The objective of this work is to develop a field procedure to determine a minimum characteristic length forming a connected fracture network in crystalline rocks. This minimum length is determined as a percolation threshold defined by models based on Percolation Theory and fracture density data determined from borehole imaging with well-logging probes. The characteristic length (3.9 meters) once estimated for a testing well was evaluated by monitoring its water head meanwhile nearby wells at different distances were pumped. The water head variation recorded in the testing well was disturbed by interfering effects associated to earth tides and atmospheric pressure, requiring further corrections to isolate effects induced by pumping. The results of these tests validate the percolation limit determined from logging data suggesting a rock mass with very low connectivity. The proposed test can be regarded as simple and easy to apply in many practical situations, for example when evaluating groundwater resources or geotechnical properties in fractured crystalline rocks.

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No. of bitstreams: 1 Dissertação - Joacir Marques de Oliveira Júnior.pdf: 5665291 bytes, checksum: 5db2b29d425ab1c0844713edba8edb09 (MD5) Previous issue date: 2014-02-14 / Among several steps which are necessary for the commercialization of oil, the analysis of well logs plays an important role to estimate the capacity of a well. Traditionally, this analysis is conducted in a semi-automated process which generates graphs of curves used by human experts to analyze and make the reservoir characterization. One goal of this analysis is to classify lithofacies. Lithofacies are lithological units(rocks) that characterize the environment and compositional aspects of the rocks. In order to characterize an oil reservoir, a set of classes of sedimentary rocks occur. This is which is the major reason for the classification of lithofacies. This master thesis investigates the use of automatic classification techniques applied to the problem of classification of lithofacies. The following five classification methods are investigated: Support Vector Machines, k-Nearest Neighbor, Multilayer Perceptron and Logistic Regression. The database investigated consists of samples from three oil wells of the same reservoir in the Amazon State. In addition, the performance of individual classifiers are compared to the combination of the same five classifiers through majority voting. Finally, we will verify whether or not individual classifiers, or ensemble of classifiers, may train using data obtained from one well and accurately classify data from other wells. In order to get these answers, we have run two series of experiments. First, we trained classifiers and test classifiers individually and combined within the same oil well. The obtained results show that Support Vector Machines achieved the best results in two of the three wells, while Multilayer Perceptron ouperformed the other methods in the third well. In the second series of experiments, we trained classifiers with data from a well and them with data from another well, simulating a situation closer to a real application, since we may use a manually classified database to train a classifier, or ensemble of classifiers, in orde to learn the pattern of the reservoir. Then, data from other wells of the same reservoir may be automatically classified. In this test, the ensemble of classifiers outperformed individual classifiers in 4 of the 6 possible combinations. In the two other combinations, the combination by majority vote was the second best. It is also worth saying that in average, ensemble of classifiers was the best option to classify lithofacies. Our results indicate that combining classifiers in a system of majority voting, shows a better performance and better stability of the results. / Dentro das várias etapas que são necessárias até o petróleo ser comercializado, a análise de perfis elétricos representa papel de grande importância para se estimar a capacidade produtiva de um poço. A análise hoje é semi-automatizada e ocorre da seguinte forma: geólogos especialistas analisam gráficos de curvas gerados por um sistema, para então, realizar a caracterização do reservatório com base nas análises. Um dos objetivos dessa análise é a classificação de litofácies. Litofácies são unidades litológicas (rochas) que caracterizam o ambiente de formação e aspectos composicionais das rochas. Para que se forme um reservatório de petróleo, um conjunto de tipos de rochas precisa estar presente, sendo este um dos principais motivos para a classificação de litofácies. Esta dissertação de mestrado investiga o uso de técnicas de classificação automática aplicadas ao problema de classificação de litofácies. Serão investigados os seguintes cinco métodos de classificação: Support Vector Machines, k Vizinhos Mais Próximos, Multilayer Perceptron e Regressão Logistica. A base de dados investigada é composta por amostras de perfis de três poços de uma reserva da Amazônia. Será ainda comparado o desempenho de classificadores individuais frente à combinação do mesmos através do voto majoritário. Por fim, iremos verificar se o treinamento de um poço pode ser aproveitado para outro por meio de classificadores individuais e combinados por voto majoritário. Para obter essas respostas, fizemos dois tipos de testes. No primeiro, treinamos e testamos os classificadores individualmente e combinados dentro do mesmo poço. Os resultados apresentados mostraram que Support Vector Machines foi superior em dois dos três poços, enquanto Multilayer Perceptron, superou os demais métodos no terceiro poço. No segundo tipo de testes, treinamos com dados de um poço e testamos com dados de outro poço, simulando uma situação mais próxima do problema real que seria de calibrar os classificadores de uma reserva com um poço pioneiro e a partir daí replicar nos poços vizinhos. Nestes testes, a combinação de classificadores se mostrou a melhor solução em 4 das 6 combinações possíveis. Nas duas demais combinações, a combinação por voto majoritário alcançou o segundo melhor resultado. Vale dizer ainda que na média simples o sistema de votação majoritário, foi a melhor opção para classificar as litofácies. Nossos resultados indicam que combinar classificadores em um sistema de votação majoritário apresenta desempenho superior ao uso de classificadores individuais, além de apresentar maior estabilidade.

Reliable assessment of static and dynamic petrophysical properties of hydrocarbon-bearing reservoirs is critical for estimating hydrocarbon reserves, identifying good production zones, and planning hydro-fracturing jobs. Conventional well-log interpretation methods are adequate to estimate static petrophysical properties (i.e., porosity and water saturation) in formations consisting of thick beds. However, they are not as reliable when estimating dynamic petrophysical properties such as absolute permeability, movable hydrocarbon saturation, and saturation-dependent capillary pressure and relative permeability. Additionally, conventional well-log interpretation methods do not take into account shoulder-bed effects, radial distribution of fluid saturations due to mud-filtrate invasion, and differences in the volume of investigation of the various measurements involved in the calculations. This dissertation introduces new quantitative methods for petrophysical and compositional evaluation of water- and hydrocarbon-bearing formations based on the combined numerical simulation and nonlinear joint inversion of conventional well logs. Specific interpretation problems considered are those associated with (a) complex mineral compositions, (b) mud-filtrate invasion, and (c) shoulder-bed effects. Conventional well logs considered in the study include density, photoelectric factor (PEF), neutron porosity, gamma-ray (GR), and electrical resistivity. Depending on the application, estimations yield static petrophysical properties, dynamic petrophysical properties, and volumetric/weight concentrations of mineral constituents. Assessment of total organic carbon (TOC) is also possible in the case of hydrocarbon-bearing shale. Interpretation methods introduced in this dissertation start with the detection of bed boundaries and population of multi-layer petrophysical properties with conventional petrophysical interpretation results or core/X-Ray Diffraction (XRD) data. Differences between well logs and their numerical simulations are minimized to estimate final layer-by-layer formation properties. In doing so, the interpretation explicitly takes into account (a) differences in the volume of investigation of the various well logs involved, (b) the process of mud-filtrate invasion, and (c) the assumed rock-physics model. Synthetic examples verify the accuracy and reliability of the introduced interpretation methods and quantify the uncertainty of estimated properties due to noisy data and incorrect bed boundaries. Several field examples describe the successful application of the methods on (a) the assessment of residual hydrocarbon saturation in a tight-gas sand formation invaded with water-base mud (WBM) and a hydrocarbon-bearing siliciclastic formation invaded with oil-base mud (OBM), (b) estimation of dynamic petrophysical properties of water-bearing sands invaded with OBM, (c) estimation of porosity and volumetric concentrations of mineral and fluid constituents in carbonate formations, and (d) estimation of TOC, total porosity, total water saturation, and volumetric concentrations of mineral constituents in the Haynesville shale-gas formation. Comparison of results against those obtained with conventional petrophysical interpretation methods, commercial multi-mineral solvers, and core/XRD data confirm the advantages and flexibility of the new interpretation techniques introduced in this dissertation for the quantification of petrophysical and compositional properties in a variety of rock formations. / text

No description available.

Under current technical levels and industrial systems, geological carbon sequestration (GCS) is a viable solution to maintain and further reduce carbon dioxide (CO2) concentration and ensure energy security simultaneously. The pre-injection formation characterization and post-injection CO2 monitoring, verification, and accounting (MVA) are two critical and challenging tasks to guarantee the sequestration effect. The tasks can be accomplished using core analyses and well-logging technologies, which complement each other to produce the most accurate and sufficient subsurface information for pore-scale and reservoir-scale studies. In recent years, the unprecedented data sources, increasing computational capability, and the developments of machine learning (ML) and deep learning (DL) algorithms provide novel perspectives for expanding the knowledge from data, which can capture highly complex nonlinear relationships between multivariate inputs and outputs. This work applied ML and DL methods to GCS-related studies at pore and reservoir scales, including digital rock physics (DRP) and the well-logging data interpretation and analysis. DRP provides cost-saving and practical core analysis methods, combining high-resolution imaging techniques, such as the three-dimensional (3D) X-ray computed tomography (CT) scanning, with advanced numerical simulations. Image segmentation is a crucial step of the DRP framework, affecting the accuracy of the following analyses and simulations. We proposed a DL-based workflow for boundary and small target segmentation in digital rock images, which aims to overcome the main challenge in X-ray CT image segmentation, partial volume blurring (PVB). The training data and the model architecture are critical factors affecting the performance of supervised learning models. We employed the entropy-based-masking indicator kriging (IK-EBM) to generate high-quality training data. The performance of IK-EBM on segmentation affected by PVB was compared with some commonly used image segmentation methods on the synthetic data with known ground truth. We then trained and tested the UNet++ model with nested architecture and redesigned skip connections. The evaluation metrics include the pixel-wise (i.e. F1 score, boundary-scaled accuracy, and pixel-by-pixel comparison) and physics-based (porosity, permeability, and CO2 blob curvature distributions) accuracies. We also visualized the feature maps and tested the model generalizations. Contact angle (CA) distribution quantifies the rock surface wettability, which regulates the multiphase behaviors in the porous media. We developed a DL-based CA measurement workflow by integrating an unsupervised learning pipeline for image segmentation and an open-source CA measurement tool. The image segmentation pipeline includes the model training of a CNN-based unsupervised DL model, which is constrained by feature similarity and spatial continuity. In addition, the over-segmentation strategy was adopted for model training, and the post-processing was implemented to cluster the model output to the user-desired target. The performance of the proposed pipeline was evaluated using synthetic data with known ground truth regarding the pixel-wise and physics-based evaluation metrics. The resulting CA measurements with the segmentation results as input data were validated using manual CA measurements. The GCS projects in the Illinois Basin are the first large-scale injection into saline aquifers and employed the latest pulsed neutron tool, the pulsed neutron eXtreme (PNX), to monitor the injected CO2 saturation. The well-logging data provide valuable references for the formation evaluation and CO2 monitoring in GCS in saline aquifers at the reservoir scale. In addition, data-driven models based on supervised ML and DL algorithms provide a novel perspective for well-logging data analysis and interpretation. We applied two commonly used ML and DL algorithms, support vector machine regression (SVR) and artificial neural network (ANN), to the well-logging dataset from GCS projects in the Illinois Basin. The dataset includes the conventional well-logging data for mineralogy and porosity interpretation and PNX data for CO2 saturation estimation. The model performance was evaluated using the root mean square error (RMSE) and R2 score between model-predicted and true values. The results showed that all the ML and DL models achieved excellent accuracies and high efficiency. In addition, we ranked the feature importance of PNX data in the CO2 saturation estimation models using the permutation importance algorithm, and the formation sigma, pressure, and temperature are the three most significant factors in CO2 saturation estimation models. The major challenge for the CO2 storage field projects is the large-scale real-time data processing, including the pore-scale core and reservoir-scale well-logging data. Compared with the traditional data processing methods, ML and DL methods achieved accuracy and efficiency simultaneously. This work developed ML and DL-based workflows and models for X-ray CT image segmentation and well-logging data interpretations based on the available datasets. The performance of data-driven surrogate models has been validated regarding comprehensive evaluation metrics. The findings fill the knowledge gap regarding formation evaluation and fluid behavior simulation across multiple scales, ensuring sequestration security and effect. In addition, the developed ML and DL workflows and models provide efficient and reliable tools for massive GCS-related data processing, which can be widely used in future GCS projects. / Doctor of Philosophy / Geological carbon sequestration (GCS) is the solution to ease the tension between the increasing carbon dioxide (CO2) concentrations in the atmosphere and the high dependence of human society on fossil energy. The sequestration requires the injection formation to have adequate storage capability, injectivity, and impermeable caprock overlain. Also, the injected CO2 plumes should be monitored in real-time to prevent any migration of CO2 to the surface. Therefore, pre-injection formation characterization and post-injection CO2 saturation monitoring are two critical and challenging tasks to guarantee the sequestration effect and security, which can be accomplished using the combination of pore-scale core analyses and reservoir-scale well-logging technologies. This work applied machine learning (ML) and deep learning (DL) methods to GCS-related studies across multiple spatial scales. We developed supervised and unsupervised DL-based workflows to segment the X-ray computed-tomography (CT) image of digital rocks for the pore-scale studies. Image segmentation is a crucial step in the digital rock physics (DRP) framework, and the following analyses and simulations are conducted on the segmented images. We also developed ML and DL models for well-logging data interpretation to analyze the mineralogy and estimate CO2 saturation. Compared with the traditional well-logging analysis methods, which are usually time-consuming and prior knowledge-dependent, the ML and DL methods achieved comparable accuracy and much shorter processing time. The performance of developed workflows and models was validated regarding comprehensive evaluation metrics, achieving excellent accuracies and high efficiency simultaneously. We are at the early stage of CO2 sequestration, and relevant knowledge and tools are inadequate. In addition, the main challenge of CO2 sequestration field projects is the large-scale and real-time data processing for fast decision-making. The findings of this dissertation fill the knowledge gap in GCS-related formation evaluation and fluid behavior simulations across multiple spatial scales. The developed ML and DL workflows provide efficient and reliable tools for massive data processing, which can be widely used in future GCS projects.

[pt] Esta tese apresenta um estudo sobre técnicas de modelagem numérica utilizadas na análise da propagação eletromagnética em formações geofísicas comumente encontradas na perfuração de poços de petróleo. O emprego de sensores eletromagnéticos adjacentes à broca de perfuração permite a inferência dos parâmetros constitutivos do solo ao redor do poço. Nos últimos anos, os avanços da tecnologia de perfilagem eletromagnética permitiram a modelagem em tempo real do problema, possibilitando direcionar a perfuração do poço a fim de maximizar a exploração de petróleo, gás, e outros hidrocarbonetos fósseis. Formações geofísicas complexas são predominantes neste tipo de problema, e geralmente são modeladas usando técnicas numéricas de força bruta como os métodos de diferenças finitas, dos elementos finitos ou dos volumes finitos. No entanto, estas técnicas têm um custo computacional relativamente alto em termos de memória e tempo de processamento. O avanço da tecnologia de perfilagem em tempo real requer abordagens mais eficientes. Neste trabalho nós empregamos o método do casamento de modos combinado com uma série de características positivas dos métodos pseudoanalíticos conhecidos na literatura para obter uma técnica inédita que permite analisar poços direcionais com estratificações radiais e longitudinais em formações geofísicas anisotrópicas. A técnica proposta permite modelar problemas ainda não explorados, mas com motivação tecnológica iminente, como a propagação eletromagnética ao longo de poços curvados e a perfuração em camadas inclinadas em relação ao eixo axial do poço. Nós apresentamos uma série de resultados de validação que demonstram que a técnica introduzida neste trabalho pode modelar de forma acurada e eficiente sensores de perfilagem eletromagnética usados na exploração de petróleo e gás. / [en] This research presents a study on numerical techniques to model the electromagnetic propagation in geophysical formations commonly encountered in oil well drilling. The employment of electromagnetic sensors surrounding the drill bit allows inferring the constitutive parameters of the soil around the well. In recent years, advances in electromagnetic logging technology have enabled the real-time modeling of this problem. In this way, the drilling direction can be guided in order to maximize the exploitation of oil, gas, and other fossil hydrocarbons. The complex geophysical formations that are prevalent in this type of problem can be effectively handled using brute-force numerical techniques such as finite-differences, finite-elements and finite-volumes. However, these techniques suffer from relatively high cost in terms of both computer memory and CPU time. The advancement of real-time logging technology demands approaches that are more efficient than purely numerical methods. In this work, we employ the mode-matching technique combining attractive features of the well-known pseudo-analytical approaches to obtain a new technique for analyzing directional well-logging tools in anisotropic formations with both radial and axial stratifications. The proposed technique allows to model problems not yet explored, but with a strong technological motivation, such as electromagnetic propagation along curved wells and drilling along inclined layers. We present a series of validation results showing that the novel technique introduced in this study can model accurately and efficiently electromagnetic logging sensors used in oil and gas exploration.

A seqüência mesozóica da bacia de Ucayali é a maior produtora de gás e condensado do Peru. A área do trabalho, denominada Grande Camisea, fica na parte sul da bacia e, na atualidade, pertence à companhia Plupetrol Peru Corporation. Neste trabalho, foi aplicado um método de interpretação de perfis de indução em um poço petrolífero no sector San Martin do campo Camisea (QuickLook Interpretation method). O objetivo consiste na caracterização do reservatório de San Martín utilizando um método de interpretação rápida de perfis elétricos e, assim, fornecer uma visão geral no entendimento de parâmetros de poços e reservatórios, de zonas produtivas e suas características petrofísicas de porosidade e de saturação do óleo. Para validar a interpretação, foram utilizadas a descrição geológica de testemunhos e amostras de calha, descrição e informação do sistema petrolífero do campo e a geologia regional da zona de interesse da bacia. Desta forma, foi possível apresentar uma comparação entre os valores obtidos através dos métodos detalhados executados pela Pluspetrol e o método rápido de interpretação aplicado aqui, assim como o desvio entre ambos os resultados. / The Mesozoic sequence of the Ucayali basin is the main producer of gas and condensate of Peru. The work area is called Gran Camisea, located in the south part of the basin, and, in the present time, belongs to the company Plupetrol Peru Corporation. In this work, a well log interpretation method was used in a gas well in San Martin area, part of the Camisea field. The goal is the characterization of the reservoir of San Martín using a Quick Look log interpretation method, and thus to supply a general view in the understanding of well and reservoirs parameters, productive zones and its petrophysics characteristics of porosity and saturation. To validate the interpretation, besides using the geologic description of well cores and cutting sampling, it was used the description and information of the petroleum system of Camisea gas field and its regional geology. It was possible to present a comparison between Pluspetrol values, obtained through detailed methods, and those from the Quick Look log interpretation method used here, as well as an analysis of convergence between both results.

A seqüência mesozóica da bacia de Ucayali é a maior produtora de gás e condensado do Peru. A área do trabalho, denominada Grande Camisea, fica na parte sul da bacia e, na atualidade, pertence à companhia Plupetrol Peru Corporation. Neste trabalho, foi aplicado um método de interpretação de perfis de indução em um poço petrolífero no sector San Martin do campo Camisea (QuickLook Interpretation method). O objetivo consiste na caracterização do reservatório de San Martín utilizando um método de interpretação rápida de perfis elétricos e, assim, fornecer uma visão geral no entendimento de parâmetros de poços e reservatórios, de zonas produtivas e suas características petrofísicas de porosidade e de saturação do óleo. Para validar a interpretação, foram utilizadas a descrição geológica de testemunhos e amostras de calha, descrição e informação do sistema petrolífero do campo e a geologia regional da zona de interesse da bacia. Desta forma, foi possível apresentar uma comparação entre os valores obtidos através dos métodos detalhados executados pela Pluspetrol e o método rápido de interpretação aplicado aqui, assim como o desvio entre ambos os resultados. / The Mesozoic sequence of the Ucayali basin is the main producer of gas and condensate of Peru. The work area is called Gran Camisea, located in the south part of the basin, and, in the present time, belongs to the company Plupetrol Peru Corporation. In this work, a well log interpretation method was used in a gas well in San Martin area, part of the Camisea field. The goal is the characterization of the reservoir of San Martín using a Quick Look log interpretation method, and thus to supply a general view in the understanding of well and reservoirs parameters, productive zones and its petrophysics characteristics of porosity and saturation. To validate the interpretation, besides using the geologic description of well cores and cutting sampling, it was used the description and information of the petroleum system of Camisea gas field and its regional geology. It was possible to present a comparison between Pluspetrol values, obtained through detailed methods, and those from the Quick Look log interpretation method used here, as well as an analysis of convergence between both results.

A seqüência mesozóica da bacia de Ucayali é a maior produtora de gás e condensado do Peru. A área do trabalho, denominada Grande Camisea, fica na parte sul da bacia e, na atualidade, pertence à companhia Plupetrol Peru Corporation. Neste trabalho, foi aplicado um método de interpretação de perfis de indução em um poço petrolífero no sector San Martin do campo Camisea (QuickLook Interpretation method). O objetivo consiste na caracterização do reservatório de San Martín utilizando um método de interpretação rápida de perfis elétricos e, assim, fornecer uma visão geral no entendimento de parâmetros de poços e reservatórios, de zonas produtivas e suas características petrofísicas de porosidade e de saturação do óleo. Para validar a interpretação, foram utilizadas a descrição geológica de testemunhos e amostras de calha, descrição e informação do sistema petrolífero do campo e a geologia regional da zona de interesse da bacia. Desta forma, foi possível apresentar uma comparação entre os valores obtidos através dos métodos detalhados executados pela Pluspetrol e o método rápido de interpretação aplicado aqui, assim como o desvio entre ambos os resultados. / The Mesozoic sequence of the Ucayali basin is the main producer of gas and condensate of Peru. The work area is called Gran Camisea, located in the south part of the basin, and, in the present time, belongs to the company Plupetrol Peru Corporation. In this work, a well log interpretation method was used in a gas well in San Martin area, part of the Camisea field. The goal is the characterization of the reservoir of San Martín using a Quick Look log interpretation method, and thus to supply a general view in the understanding of well and reservoirs parameters, productive zones and its petrophysics characteristics of porosity and saturation. To validate the interpretation, besides using the geologic description of well cores and cutting sampling, it was used the description and information of the petroleum system of Camisea gas field and its regional geology. It was possible to present a comparison between Pluspetrol values, obtained through detailed methods, and those from the Quick Look log interpretation method used here, as well as an analysis of convergence between both results.