Modern scanning electron microscopes often include software that allows for the possibility of obtaining large format high-resolution image montages over areas of several square centimeters. Such montages are typically automatically acquired and stitched, comprising many thousand individual tiled images. Images, collected over a regular grid pattern, are a rich source of information on factors such as variability in porosity and distribution of mineral phases, but can be hard to visually interpret. Additional quantitative data can be accessed through the application of image analysis. We use backscattered electron (BSE) images, collected from polished thin sections of two limestone samples from the Cretaceous of Brazil, a Carboniferous limestone from Scotland, and a carbonate cemented sandstone from Northern Ireland, with up to 25,000 tiles per image, collecting numerical quantitative data on the distribution of porosity. Images were automatically collected using the FEI software Maps, batch processed by image analysis (through ImageJ), with results plotted on 2D contour plots with MATLAB. These plots numerically and visually clearly express the collected porosity data in an easily accessible form, and have application for the display of other data such as pore size, shape, grain size/shape, orientation and mineral distribution, as well as being of relevance to sandstone, mudrock and other porous media.

Understanding the impact of fractures on fluid flow is fundamental for developing geoenergy reservoirs. Pressure transient analysis could play a key role for fracture characterization purposes if better links can be established between the pressure derivative responses (p′) and the fracture properties. However, pressure transient analysis is particularly challenging in the presence of fractures because they can manifest themselves in many differentp′ curves. In this work, we aim to provide a proof-of-concept machine learning approach that allows us to effectively handle the diversity in fracture-relatedp′ curves by automatically classifying them and identifying the characteristic fracture patterns. We created a synthetic dataset from numerical simulation that comprised 2560p′ curves that represent a wide range of fracture network properties. We developed an unsupervised machine learning approach that can distinguish the temporal variations in thep′ curves by combining dynamic time warping with k-medoids clustering. Our results suggest that the approach is effective at recognizing similar shapes in thep′ curves if the second pressure derivatives are used as the classification variable. Our analysis indicated that 12 clusters were appropriate to describe the full collection ofp′ curves in this particular dataset. The classification exercise also allowed us to identify the key geological features that influence thep′ curves in this particular dataset, namely (1) the distance from the wellbore to the closest fracture(s), (2) the local/global fracture connectivity, and (3) the local/global fracture intensity. With additional training data to account for a broader range of fracture network properties, the proposed classification method could be expanded to other naturally fractured reservoirs and eventually serve as an interpretation framework for understanding how complex fracture network properties impact pressure transient behaviour.

The thick and heterogeneous salt section in the Santos Basin, offshore Brazil, imposes great challenges in accessing the pre-salt hydrocarbon reservoirs, especially in relation to seismic imaging, signal quality and depth positioning. Some problems arise from the current velocity models for the salt section, which, for the majority, assume that the salt is a homogeneous halite layer. In the Santos Basin, the commonly assumed salt – halite – only makes up to 80% of the mineral in this section. The inclusion of other salts as stratification in the velocity models, based on seismic attributes, has achieved good results in the last decade, especially for depth resolution. In this work, we analyse the benefits of different velocity models, considering presence/absence of salt stratification and comparing the gross rock volume above the oil–water contact. The results show a significant effect on the depth resolution of the events, as well as on volume estimation, indicating that the greater the reliability captured by the complex velocity models, the greater the confidence in the resulting volumetric information.

Nuclear magnetic resonance (NMR) is a recognized petrophysical tool in the oil and gas industry to characterize reservoir rocks and fluids. In this study, the pore system of coquinas from a single bed of a quarry in the Morro do Chaves Formation was evaluated. These sedimentary rocks have been considered as a potential analogous to some Brazilian pre-salt reservoir rocks. The objective of this work was to characterize the porous system of coquinas in terms of total porosity and pore size distribution using low-field NMR. Conversion of T2 relaxation times to pore size radii was performed and literature cut-offs were applied for porosity partitioning. Coquinas were classified and ranked according to their percentage of macro, meso and micro porosity. This work verified quantitatively the pore system heterogeneities for the coquina samples and the variation in the layer from which they were extracted. The study provides some clues on lateral porosity and pore size variation in any reservoir for which this unit is an analogue.Keywords: Petrophysics, NMR, Total Porosity, Pore Size Distribution, Porosity Partitioning.RESUMO. Ressonância magnética nuclear (RMN) é uma técnica petrofísica reconhecida na indústria de óleo e gás pela sua capacidade de caracterizar rochas reservatório e seus fluidos saturantes. Neste estudo, foi avaliado o sistema poroso de coquinas pertencentes à uma camada de uma pedreira na Formação Morro do Chaves. Essas rochas sedimentares foram consideradas possíveis análogos de algumas rochas carbonáticas do pré-sal brasileiro. O objetivo do trabalho foi caracterizar o sistema poroso dessas coquinas em termos de porosidade total e distribuição de tamanho de poros utilizando RMN de baixo campo. Realizou-se a conversão dos tempos de relaxação T2 para raios de poro e empregou-se cut-offs da literatura para o particionamento da porosidade. As coquinas foram classificadas e ranqueadas de acordo com a sua porcentagem de macro, meso e micro poros. Verificou-se quantitativamente a heterogeneidade do sistema poroso das coquinas estudadas e a variação da camada sedimentar em que os plugues foram retirados. O estudo fornece informações sobre a variação lateral de porosidade e tamanho de poros para reservatórios que tenham a unidade estudada como análogo.Palavras-chave: Petrofísica, RMN, Porosidade Total, Distribuição do Tamanho de Poros, Particionamento da Porosidade.