Image-Based Modeling of Porous Media Using FEM and Lagrangian Particle Tracking

The study of fundamental flow and transport processes at the pore scale is essential to understanding how the mechanisms affect larger, field-scale, processes that occur in oil and gas recovery, groundwater flow, contaminant transport, and CO2 sequestration. Pore-scale imaging and modeling is one of the techniques used to investigate these fundamental mechanisms. Although extensive development of pore-scale imaging and modeling has occurred recently, some areas still need further advances. In this work, we address two areas: (1) imaging of bulk proppants and proppant-filled fractures under varying loading stress and flow simulation in these systems and (2) nanoparticle (NP) transport modeling in porous media. These are briefly explained below. Rock fracturing, followed by proppant injection, has been used for years to improve oil and gas production rates in low permeability reservoirs and is now routinely used in low-permeability resources such as a shales and tight sands. While field data makes clear the effectiveness of this technique, there is still much room to improve on the science, including how the proppant-filled fracture system responds to changes in loading stress which affect permeability and conductivity. Here, we use high-resolution x-ray computed tomography (XCT) to image two unsaturated rock/fracture/proppant systems under a series of stress levels typical of producing reservoirs: one with shale, one with Berea sandstone. The resulting XCT images were segmented, analyzed for structural and porosity changes, and then used for image-based flow modeling of Stokes flow using both finite element (FEM) and Lattice Boltzmann methods. NPs have been widely used commercially and have the potential to be extensively used in petroleum engineering as stabilizers in enhanced oil recovery operations or as tracers or sensors to detect rock and fluid properties. %In spite of a wide range of applications, many NP transport details are still unknown. In this work, we describe a Lagrangian particle tracking algorithm to model NP transport that can be used to better understand the impact of pore-scale hydrodynamics and surface forces on NP transport. Two XCT images, a Berea sandstone and a 2.5D micromodel, were meshed and used for image-based flow modeling of FEM Stokes flow. The effects of particle size, surface forces, flow rate, particle density, surface capacity, and surface forces mapped to XCT-image based mineralogy were studied

DUAL-MODALITY (NEUTRON AND X-RAY) IMAGING FOR CHARACTERIZATION OF PARTIALLY SATURATED GRANULAR MATERIALS AND FLOW THROUGH POROUS MEDIA

Problems involving mechanics of partially saturated soil and physics of flow through porous media are complex and largely unresolved based on using continuum approach. Recent advances in radiation based imaging techniques provide unique access to simultaneously observe continuum scale response while probing corresponding microstructure for developing predictive science and engineering tools in place of phenomenological approach used to date. Recent developments with X-ray/Synchrotron and neutron imaging techniques provided tools to visualize the interior of soil specimen at pore/grain level. X-ray and neutron radiation often presents complementary contrast for given condensed matter in the images due to different fundamental interaction mechanisms. While X-rays mainly interact with the electron clouds, neutrons directly interact with the nucleus of an atom. The dual-modal contrasts are well suited for probing the three phases (silica, air and water) of partially saturated sand since neutrons provide high penetration through large sample size and are very sensitive to water and X-rays of high energy can penetrate moderate sample sizes and clearly show the particle and void phases. Both neutron and X-ray imaging techniques are used to study microstructure of partially saturated compacted sand and water flow behavior through sand with different initial structures. Water distribution in compacted sand with different water contents for different grain shapes of sand was visualized with relatively coarse resolution neutron radiographs and tomograms. Dual-modal contrast of partially saturated sand was presented by using high spatial resolution neutron and X-ray imaging. Advanced image registration technique was used to combine the dual modality data for a more complete quantitative analysis. Quantitative analysis such as grain size distribution, pore size distribution, coordination number, and water saturation along the height were obtained from the image data. Predictive simulations were performed to obtain capillary pressure – saturation curves and simulated two fluid phase (water and air) distribution based image data. In-situ water flow experiments were performed to investigate the effect of initial microstructure. Flow patterns for dense and loose states of Ottawa sand specimens were compared. Flow patterns and water distribution of dense Ottawa and Q-ROK sand specimens was visualized with high resolution neutron and X-ray image data

Incorporating Boltzmann Machine Priors for Semantic Labeling in Images and Videos

Semantic labeling is the task of assigning category labels to regions in an image. For example, a scene may consist of regions corresponding to categories such as sky, water, and ground, or parts of a face such as eyes, nose, and mouth. Semantic labeling is an important mid-level vision task for grouping and organizing image regions into coherent parts. Labeling these regions allows us to better understand the scene itself as well as properties of the objects in the scene, such as their parts, location, and interaction within the scene. Typical approaches for this task include the conditional random field (CRF), which is well-suited to modeling local interactions among adjacent image regions. However the CRF is limited in dealing with complex, global (long-range) interactions between regions in an image, and between frames in a video. This thesis presents approaches to modeling long-range interactions within images and videos, for use in semantic labeling. In order to model these long-range interactions, we incorporate priors based on the restricted Boltzmann machine (RBM). The RBM is a generative model which has demonstrated the ability to learn the shape of an object and the CRBM is a temporal extension which can learn the motion of an object. Although the CRF is a good baseline labeler, we show how the RBM and CRBM can be added to the architecture to model both the global object shape within an image and the temporal dependencies of the object from previous frames in a video. We demonstrate the labeling performance of our models for the parts of complex face images from the Labeled Faces in the Wild database (for images) and the YouTube Faces Database (for videos). Our hybrid models produce results that are both quantitatively and qualitatively better than the baseline CRF alone for both images and videos

Two-phase flow in rocks : new insights from multi-scale pore network modeling and fast pore scale visualization

Many geological applications involve the flow of multiple fluids through porous geological materials, e.g. environmental remediation of polluted ground water resources, carbon dioxide storage in geological reservoirs and petroleum recovery. Commonly, to model these applications, the geological materials in question are treated as continuous porous media with effective material properties. Since these properties are a manifestation of what goes on in the pores of the material, we have to study the transport processes at the pore scale to understand why and how they vary over space and time in different rocks and under different conditions. As the high cost of acquiring and testing samples in many of these applications is often a limiting factor, numerical modelling at the pore scale is becoming a key technology to gain new insights in this field. This could be crucial in reducing uncertainties in field scale projects. The work presented in this thesis focuses on the investigation of two-phase flow in sedimentary rocks, and is an integrated numerical and experimental study. It deals primarily with two outstanding issues. First, image-based pore scale simulation methods have difficulties with representing the multiple pore scales in rocks with wide pore size distributions, due to a trade-off in the size and resolution of both modeling and imaging methods. Therefore, performing two-phase flow simulations in a number of important rock types, such as many carbonates and tight, clay-baring sandstones has remained an outstanding challenge. To tackle this problem, a new numerical model was developed to calculate capillary pressure, relative permeability and resistivity index curves during drainage and imbibition processes in such materials. The multi-scale model was based on information obtained from 3D micro-computed tomography images of the internal pore structure, complemented with information on the pores that are unresolved with this technique. In this method, pore network models were first extracted from resolved pores in the images, by using a maximal ball network extraction algorithm. Then, pores which touched regions with unresolved porosity were connected with a special type of network element called micro-links. In the quasi-static simulations that were performed on these network models, the micro-links carried average properties of the unresolved porosity. In contrast to most previous models, the new approach to taking into account unresolved porosity therefore allowed efficient simulations on images of complex rocks, with sizes comparable to single-scale pore network models. It was able to reproduce most of the behaviour of a fully resolved pore network model, for both drainage and imbibition processes, and for different pore scale wettability distributions (water-wet, oil-wet and different mixed-wet distributions). Furthermore, simulations on images of carbonate rocks showed good agreement to experiments. A sensitivity study on carbonate rocks and tight, clay-bearing sandstones produced results that were in qualitative agreement with experiments, and allowed to analyse how the two-phase flow behaviour of these rocks is influenced by their pore scale properties. The second issue which is treated in this thesis is related to the validation of pore scale models. Comparing predicted effective properties to experimentally measured values is useful and necessary, but is complicated by the typical difference in size between the model and the experiment. Furthermore, it does not always give a clear indication of the reasons for an observed mismatch between models and experiments. Comparing two-phase flow models to pore scale experiments in which the evolution of the fluid distributions is visualized is thus extremely useful. However, this requires to image the two-phase flow process while it is taking place in a rock, and it is necessary to do this with time resolutions on the order of tens of seconds and spatial resolutions on the order of micrometers. Previous experimental approaches used synchrotron beam lines to achieve this. In this thesis, we show that such experiments are also possible using laboratory-based micro-computed tomography scanners, which are orders of magnitude cheaper and therefore more accessible than synchrotrons. An experiment in which kerosene was pumped into a water-saturated sandstone is presented, showing that individual Haines jumps (pore filling events) could be visualized during this drainage process. Because the image quality is lower than at synchrotrons, care had to be taken to adapt the image analysis work flow to deal with high image noise levels. The work flow was designed to allow to track the fluid filling state of individual pores. The results indicate that the dynamic effects due to viscous and inertial forces during Haines jumps do not significantly impact the evolution of the fluid distributions during drainage, which may thus be adequately described by quasi-static models

Holistic interpretation of visual data based on topology:semantic segmentation of architectural facades

The work presented in this dissertation is a step towards effectively incorporating contextual knowledge in the task of semantic segmentation. To date, the use of context has been confined to the genre of the scene with a few exceptions in the field. Research has been directed towards enhancing appearance descriptors. While this is unarguably important, recent studies show that computer vision has reached a near-human level of performance in relying on these descriptors when objects have stable distinctive surface properties and in proper imaging conditions. When these conditions are not met, humans exploit their knowledge about the intrinsic geometric layout of the scene to make local decisions. Computer vision lags behind when it comes to this asset. For this reason, we aim to bridge the gap by presenting algorithms for semantic segmentation of building facades making use of scene topological aspects. We provide a classification scheme to carry out segmentation and recognition simultaneously.The algorithm is able to solve a single optimization function and yield a semantic interpretation of facades, relying on the modeling power of probabilistic graphs and efficient discrete combinatorial optimization tools. We tackle the same problem of semantic facade segmentation with the neural network approach.We attain accuracy figures that are on-par with the state-of-the-art in a fully automated pipeline.Starting from pixelwise classifications obtained via Convolutional Neural Networks (CNN). These are then structurally validated through a cascade of Restricted Boltzmann Machines (RBM) and Multi-Layer Perceptron (MLP) that regenerates the most likely layout. In the domain of architectural modeling, there is geometric multi-model fitting. We introduce a novel guided sampling algorithm based on Minimum Spanning Trees (MST), which surpasses other propagation techniques in terms of robustness to noise. We make a number of additional contributions such as measure of model deviation which captures variations among fitted models

Machine learning of image analysis with convolutional networks and topological constraints

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 130-140).We present an approach to solving computer vision problems in which the goal is to produce a high-dimensional, pixel-based interpretation of some aspect of the underlying structure of an image. Such tasks have traditionally been categorized as ''low-level vision'' problems, and examples include image denoising, boundary detection, and motion estimation. Our approach is characterized by two main elements, both of which represent a departure from previous work. The first is a focus on convolutional networks, a machine learning strategy that operates directly on an input image with no use of hand-designed features and employs many thousands of free parameters that are learned from data. Previous work in low-level vision has been largely focused on completely hand-designed algorithms or learning methods with a hand-designed feature space. We demonstrate that a learning approach with high model complexity, but zero prior knowledge about any specific image domain, can outperform existing techniques even in the challenging area of natural image processing. We also present results that establish how convolutional networks are closely related to Markov random fields (MRFs), a popular probabilistic approach to image analysis, but can in practice can achieve significantly greater model complexity. The second aspect of our approach is the use of domain specific cost functions and learning algorithms that reflect the structured nature of certain prediction problems in image analysis.(cont.) In particular, we show how concepts from digital topology can be used in the context of boundary detection to both evaluate and optimize the high-order property of topological accuracy. We demonstrate that these techniques can significantly improve the machine learning approach and outperform state of the art boundary detection and segmentation methods. Throughout our work we maintain a special interest and focus on application of our methods to connectomics, an emerging scientific discipline that seeks high-throughput methods for recovering neural connectivity data from brains. This application requires solving low-level image analysis problems on a tera-voxel or peta-voxel scale, and therefore represents an extremely challenging and exciting arena for the development of computer vision methods.by Viren Jain.Ph.D

Simulation study on PEM fuel cell gas diffusion layers using x-ray tomography based Lattice Boltzmann method

The Polymer Electrolyte Membrane (PEM) fuel cell has a great potential in leading the future energy generation due to its advantages of zero emissions, higher power density and efficiency. For a PEM fuel cell, the Membrane-Electrode Assembly (MEA) is the key component which consists of a membrane, two catalyst layers and two gas diffusion layers (GDL). The success of optimum PEM fuel cell power output relies on the mass transport to the electrode especially on the cathode side. The carbon based GDL is one of the most important components in the fuel cell since it has one of the basic roles of providing path ways for reactant gases transport to the catalyst layer as well as excess water removal. A detailed understanding and visualization of the GDL from micro-scale level is limited by traditional numerical tool such as CFD and experimental methods due to the complex geometry of the porous GDL structural. In order to take the actual geometry information of the porous GDL into consideration, the x-ray tomography technique is employed which is able to reconstructed the actual structure of the carbon paper or carbon cloth GDLs to three-dimensional digital binary image which can be read directly by the LB model to carry out the simulation. This research work contributes to develop the combined methodology of x-ray tomography based the three-dimensional single phase Lattice Boltzmann (LB) simulation. This newly developed methodology demonstrates its capacity of simulating the flow characteristics and transport phenomena in the porous media by dealing with collision of the particles at pore-scale. The results reveal the heterogeneous nature of the GDL structures which influence the transportation of the reactants in terms of physical parameters of the GDLs such as porosity, permeability and tortuosity. The compression effects on the carbon cloth GDLs have been investigated. The results show that the c applied compression pressure on the GDLs will have negative effects on average pore size, porosity as well as through-plane permeability. A compression pressure range is suggested by the results which gives optimum in-plane permeability to through-plane permeability. The compression effects on one-dimensional water and oxygen partial pressures in the main flow direction have been studied at low, medium and high current densities. It s been observed that the water and oxygen pressure drop across the GDL increase with increasing the compression pressure. Key Words: PEM fuel cell, GDL, LB simulation, SPSC, SPMC, x-ray tomography, carbon paper, carbon cloth, porosity, permeability, degree of anisotropy, tortuosity, flow transport