Integrating Deep Learning into Digital Rock Analysis Workflow

Digital Rock Analysis (DRA) has expanded our knowledge about natural phenomena in various geoscience specialties. DRA as an emerging technology has limitations including (1) the trade-off between the size of spatial domain and resolution, (2) methodological and human-induced errors in segmentation, and (3) the computational costs associated with intensive modeling. Deep learning (DL) methods are utilized to alleviate these limitations. First, two DL frameworks are utilized to probe the performance gains from using Convolutional Neural Networks (CNN) to super-resolve and segment real multi-resolution X-ray images of complex carbonate rocks. The first framework experiments the applications of U-Net and U-ResNet architectures to obtain macropore, solid, and micropore segmented images in an end-to-end scheme. The second framework segregates the super-resolution and segmentation into two networks: EDSR and U-ResNet. Both frameworks show consistent performance indicated by the voxel-wise accuracy metrics, the measured phase morphology, and flow characteristics. The end-to-end frameworks are shown to be superior to using a segregated approach confirming the adequacy of end-to-end learning for performing complex tasks. Second, CNNs accuracy margins in estimating physical properties of porous media 2d X-ray images are investigated. Binary and greyscale sandstone images are used as an input to CNNs architectures to estimate porosity, specific surface area, and average pore size of three sandstone images. The results show encouraging margins of accuracy where the error in estimating these properties can be up to 6% when using binary images and up to 7% when using greyscale images. Third, the suitability of CNNs as regression tools to predict a more challenging property, permeability, is investigated. Two complex CNNs architectures (ResNet and ResNext) are applied to learn the morphology of pore space in 3D porous media images for flow-based characterization. The dataset includes more than 29,000 3d subvolumes of multiple sandstone and carbonates rocks. The findings show promising regression accuracy using binary images. Accuracy gains are observed using conductivity maps as an input to the networks. Permeability inference on unseen samples can be achieved in 120 ms/sample with an average relative error of 18.9%. This thesis demonstrates the significant potential of deep learning in improving DRA capabilities

Reservoir Characterisation of Gas Shale through Sedimentary, Mineralogical, Petrophysical and Statistical Rock Types Evaluation

The successful exploration and production of the gas shale reservoirs can help to face the current energy crisis. However, shale is a fine-grained heterogeneous rock, so its exploration and development are challenging. This research has provided an integrated method for analysis, evaluation, and synthesis of potential gas shale formations in the Canning Basin, Western Australia. The results form a valuable case study that is applicable to many other sedimentary basins throughout the world

4. International reservoir characterization technical conference

This volume contains the Proceedings of the Fourth International Reservoir Characterization Technical Conference held March 2-4, 1997 in Houston, Texas. The theme for the conference was Advances in Reservoir Characterization for Effective Reservoir Management. On March 2, 1997, the DOE Class Workshop kicked off with tutorials by Dr. Steve Begg (BP Exploration) and Dr. Ganesh Thakur (Chevron). Tutorial presentations are not included in these Proceedings but may be available from the authors. The conference consisted of the following topics: data acquisition; reservoir modeling; scaling reservoir properties; and managing uncertainty. Selected papers have been processed separately for inclusion in the Energy Science and Technology database

Estudo do comportamento da porosidade via simulação numérica para produtos injetados em alúminio sob pressão

Orientador : Prof. Dr. Paulo Victor Prestes MarcondesTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Mecânica. Defesa: Curitiba, 15/10/2015Inclui referências : f. 104-108Área de concentração : ManufaturaResumo: A porosidade resultante do processo de injeção de alumínio sob pressão corresponde a 35% da falha desse processo. A determinação da origem dos poros e definição dos mecanismos de solução é complexa, pois há vários fatores que induzem a sua ocorrência. Assim, é comum se empregar alternativas de processo e de engenharia para tentar resolver o problema. Diante de tal complexidade, procurou-se propor alternativas de como aplicar os métodos dos elementos finitos para minimizar a probabilidade da ocorrência de poros em produtos injetados em alumínio através do processo HPDC. Procurou-se, ainda, verificar quais seriam as melhores configurações de engenharia e de processo objetivando diminuir o volume de poros em produtos injetados em alumínio culminando com o desenvolvimento de uma equação que represente o comportamento e a origem da porosidade. Para estudar a origem e o comportamento da porosidade foram analisadas algumas condições de projeto e de processo, nas quais foram realizadas simulações computacionais, utilizando-se os softwares Magma e Click2Cast, para injeção de alumínio sob pressão e Jump para análise estatística de resultados. Observou-se que através de fluxo e de solidificação, pode-se entender o comportamento da porosidade em produtos injetados em alumínio sob pressão. Verificou-se ainda que a velocidade no canal de alimentação e o tempo de preenchimento não interferem na origem de porosidade, porém o menor tempo de solidificação é o parâmetro de injeção que resulta em um menor volume de porosidade. A partir do levantamento de um banco de dados, foi possível desenvolver uma equação estatística para descrever o comportamento da porosidade incluindo todos os parâmetros de injeção utilizados no processo HPDC. Palavras-chave: Processo de injeção de alumínio sob pressão. Alumínio. Porosidade. Simulação Numérica.Abstract: The resulting porosity is responsible to 35% of failures on the high pressure aluminum die casting process. The determination of the origin and setting the pore elimination is a complex mechanism. There are several factors that induce their occurrence. Thus, it is common to employ process and engineering alternatives in order to try to solve the issue. Faced with such complexity we tried to understand how to apply the finite element methods to minimize the occurrence of pores in highpressure die casting products. The motivation was to determine the best engineering and process settings to reduce the pore volume in aluminum injected products. The aim of this study was to develop a methodology in order to generate an equation that represents the porosity behavior. In order to do that, the results obtained with the variation of some boundary conditions which were applied to computer simulations in commercial dedicated software (Magma, Click2Cast and Jump) were analyzed. It was observed that a flow and solidification analysis of the product in the mold can determine the probability of occurrence of pores in the product already during injection process. Keywords: High-pressure die casting. HPDC. Aluminum. Porosity. Numerical simulation

Remote Sensing of Earth Resources: A literature survey with indexes (1970 - 1973 supplement). Section 1: Abstracts

Abstracts of reports, articles, and other documents introduced into the NASA scientific and technical information system between March 1970 and December 1973 are presented in the following areas: agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis