Andrew Tulloh

Feasibility of a data-constrained prediction of hydrocarbon reservoir sandstone microstructures

Microstructures are critical for defining material characteristics such as permeability, mechanical, electrical and other physical properties. However, the available techniques for determining compositional microstructures through segmentation of x-ray computed tomography (CT) images are inadequate when there are finer structures than the CT spatial resolution, i.e. when there is more than one material in each voxel. This is the case for CT imaging of geomaterials characterized with submicron porosity and clay coating that control petrophysical properties of rock. This note outlines our data-constrained modelling (DCM) approach for prediction of compositional microstructures, and our investigation of the feasibility of determining sandstone microstructures using multiple CT data sets with different x-ray beam energies. In the DCM approach, each voxel is assumed to contain a mixture of multiple materials, optionally including voids. Our preliminary comparisons using model samples indicate that the DCM-predicted compositional microstructure is consistent with the known original microstructure under low noise conditions. The approach is quite generic and is applicable to predictions of microstructure of various materials.

Microstructure of a Paint Primer - a Data-Constrained Modeling Analysis

Metallic aerospace components are commonly painted with a primer to improve their corrosion resistance. The primer contains a polymer matrix with embedded corrosion inhibitor and filler particles. Its performance is determined by the microscopic distributions of the particles. Various techniques have been used to quantify such distributions, including X-ray micro-computed tomography (CT). However, its success is sometimes limited by factors such as different particles having similar X-ray CT absorption properties and their size being smaller than the resolution of micro-CT. In this paper, we have performed two X-ray CT measurements on a paint primer sample consisting of SrCrO4 corrosion inhibitor particles and UV-absorbing TiO2 filler particles. Fe and Ti targets were used as X-ray sources with different spectral distributions. The measured CT data sets were used as constraints for a data-constrained microstructure modeling (DCM) prediction of the sample’s microscopic structures. DCM model predictions were compared with experimental elemental surface maps and showed reasonable degree of agreement, suggesting X-ray micro-CT combined with DCM modeling would be a powerful technique for detailing the dynamics of chromate-inhibited primers and other multiphase systems where the components are sensitive to incident X-ray energy.

Data-Constrained Microstructure Characterization with Multispectrum X-Ray Micro-CT

Conventional X-ray microcomputed tomography (micro-CT) is not usually sufficient to determine microscopic compositional distributions as it is limited to measuring the X-ray attenuation of the sample, which for a given dataset can be similar for materials of different composition. In contrast, the present work enables three-dimensional compositional analysis with a data-constrained microstructure (DCM) modeling methodology, which uses two or more CT datasets acquired with different X-ray spectra and incorporates them as model constraints. For providing input data for DCM, we have also developed a method of micro-CT data collection that enables two datasets with different X-ray spectra to be acquired in parallel. Such data are used together with the DCM methodology to predict the distributions of corrosion inhibitor and filler in a polymer matrix. The DCM-predicted compositional microstructures have a reasonable agreement with energy dispersive X-ray images taken on the sample surface.

A Data-Constrained 3D Model for Material Compositional Microstructures

A mathematical model has been developed for predicting material compositional microstructures using measured data as constraints. Examples of measured data include 3-D sets of tomography data, 2-D sets of compositional data on surfaces and sections, and material absorption and interaction properties. The model has been partially implemented as a MS-Windows application. Reasonable agreement has been obtained between the numerical predictions from the software and the simulated data. The predicted microstructures could be used to study various material properties such as porosity distribution, diffusion and corrosion.

SENSOR-BASED WATER PARCEL TRACKING

The increased use of on-line sensors means that a number of physical and chemical parameters are often measured routinely in drinking water distribution systems. These include temperature, pH, turbidity, free and total chlorine, oxygen reduction potential (ORP), electrical conductivity (EC), and fluoride. Laboratory trials showed that real-time monitoring of such parameters following addition of suitable tracers could be used to measure the travel time of water in pipes. Inert tracers gave good estimates of travel time, while reactive tracers such as temperature pulses interacted with the pipe and were delayed. Laminar flow caused increased broadening of tracer pulses, so that turbulent flow was preferred. Inspection of on-line monitoring data also showed that these parameters fluctuate naturally with time giving the water flow a characteristic fingerprint without the need to add a tracer. Field trials showed that these fingerprints were preserved for significant periods and could be successfully detected in a local distribution system after transit times of more than one hour. Visual matching of output peaks was used to measure transit time, and some success has also been achieved using an automatic correlation routine to track water parcel movement. The fingerprint tracking technique is a unique way of following water movement without using added tracer, and it does not require detailed knowledge of network connectivity that is necessary when using a hydraulic modeling approach. This technique has considerable potential in water quality and water security applications, and should enable real-time measurements of water age and chemistry in distribution systems.

Data-constrained microstructure modeling with multi-spectrum x-ray CT

Conventional X-ray CT is not usually sufficient to determine microscopic compositional distributions. A dataconstrained microstructure modeling (DCM) methodology has been developed which uses multiple CT data sets acquired with different X-ray spectra, and incorporates them as model constraints. The DCM approach has been applied to predict the distributions of corrosion inhibitor and filler in a polymer matrix. The DCM-predicted compositional microstructures have a reasonable agreement with EDX images taken on the sample surface.

A tutorial introduction to DCM quantitative characterization and modelling of material microstructures using monochromatic multi-energy x-ray CT

This article is intended as a tutorial guide for new users of the DCM (data-constrained modelling) software for quantitative characterization of material 3D microstructures using multi-energy X-ray CT data. It guides users through the steps necessary for processing a small CIPS (Calcite In-situ Precipitation System) sandstone data set. It also covers some built-in and plug-in features to analyze and visualize the microstructures.

Characterization of Darai Limestone Composition and Porosity Using Data-Constrained Modeling and Comparison with Xenon K-Edge Subtraction Imaging.

Data-constrained modeling is a method that enables three-dimensional distribution of mineral phases and porosity in a sample to be modeled based on micro-computed tomography scans acquired at different X-ray energies. Here we describe an alternative method for measuring porosity, synchrotron K-edge subtraction using xenon gas as a contrast agent. Results from both methods applied to the same Darai limestone sample are compared. Reasonable agreement between the two methods and with other porosity measurements is obtained. The possibility of a combination of data-constrained modeling and K-edge subtraction methods for more accurate sample characterization is discussed.