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

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios