The presence of subseismic scale faulting within high porosity sandstone reservoirs and aquifers represents a significant source of uncertainty for activities such as hydrocarbon production and the geologic sequestration of carbon dioxide. The inability to resolve geometrical properties of these smaller scale faults, such as size, connectivity and intensity, using conventional subsurface datasets (i.e. seismic reflection tomography, wireline log and core), leads to ambiguous representations within reservoir models and simulators. In addition, more fundamental questions still remain over the role of cataclastic faults in the trapping and transfer of mobile geofluids within the subsurface, particularly when two or more immiscible fluid phases are present, as is the case during hydrocarbon accumulation, waterflood operations and CO2 injection. By harnessing recent developments in 3D digital surface and volume imaging, this study addresses uncertainties pertaining to the geometrical and petrophysical properties of subseismic scale faults within porous sandstone reservoirs. A novel structural feature extraction and modelling framework is developed, which facilitates the restoration of fault and fracture architecture from digital rock surface models. This framework has been used to derive volumetric fault abundance and connectivity from a normal sense array of cataclastic shear bands developed within high porosity sandstones of the Vale of Eden Basin, UK. These spatially resolved measures of discontinuity abundance provide the basis for the geostatistical extrapolation of fracture/fault intensity into reservoir modelling grids, which promises the introduction of a much higher degree of geological realism into discrete fracture network models than can currently be achieved through purely stochastic methods. Moreover, by establishing spatial correspondences between volumetric faulting intensity and larger scale features of deformation observed at the study area (cataclastic shear zones), the work demonstrates the potential to relate reservoir equivalent measures of fault or fracture abundance obtained from outcrop to seismically resolvable structures within the subsurface, aiding the prediction of reservoir structure from oilfield datasets. In addition to the derivation of continuum scale properties of sub-seismic scale fault networks, a further investigation into the pore-scale controls which govern the transfer of fluids within cataclised sandstones has been conducted. Through X-ray tomographic imaging of experimental core flood (scCO2-brine primary drainage) through a cataclastic shear band bearing sandstone, insights into the influence that variations in fault structure exert over the intra-fault drainage pathway of an invading non-wetting fluid have been gained. Drainage across the fault occurs as a highly non-uniform and non-linear process, which calls into question the practice of using continuum methods to model cross fault flow. This work has also provided an improved understanding of the role that high capillary entry pressure cataclised regions play in modifying pore-fluid displacement processes within the surrounding matrix continuum. In particular, the high sweep efficiency and enhanced non-wetting phase pore-wall contact relating to elevated phase pressure observed during drainage points towards favourable conditions for wettability alteration within cataclised sandstones. This is likely to negatively impact upon the effectiveness of oil recovery and CO2 sequestration operations within equivalent reservoir and aquifer settings.

The pursuit of more suitable drugs intended for possible biological applications are a continuously growing topic of research within the scientific community. One of these suitable qualities includes the need for hydrophilicity and or some appropriate delivery system for the drug to enter into biological systems. A system of analyzing and following these compounds would then, however, be necessary to conduct any kind of mechanistic or interaction studies for he said drug within the biological system. Just to name a few, fluorescence and X-ray computed tomography (CT) methods allow for imaging of biological systems but require the need of compounds with specific qualities. Finally, even with a means of entering and following a oaded drug, it would not be complete without a way of targeting its intended location. Herein, the first chapter reports the synthesis and characterization of a fluorene-based pyridil bis-?-diketone compound with suitable one- and two-photon fluorescent properties and its encapsulation into Pluronic F127 micelles for the possible application of tracking lysosomes. Next the synthesis and characterization of a BODIPY-based fluorophore with excellent fluorescence ability is reported. This compound was conjugated to two triphenylphosphine (TPP) groups and is shown as a potential mitochondria probe within HCT-116 cells. Finally, the synthesis and characterization of diatrizoic acid (DA) based derivatives conjugated to silica nanoparticles, as well as unconjugated, are reported as potential CT contrast agents. The derivatives were also functionalized with maleimide moieties facilitating subsequent potential bioconjugation of a targeting protein via a thiol group.

Under current technical levels and industrial systems, geological carbon sequestration (GCS) is a viable solution to maintain and further reduce carbon dioxide (CO2) concentration and ensure energy security simultaneously. The pre-injection formation characterization and post-injection CO2 monitoring, verification, and accounting (MVA) are two critical and challenging tasks to guarantee the sequestration effect. The tasks can be accomplished using core analyses and well-logging technologies, which complement each other to produce the most accurate and sufficient subsurface information for pore-scale and reservoir-scale studies. In recent years, the unprecedented data sources, increasing computational capability, and the developments of machine learning (ML) and deep learning (DL) algorithms provide novel perspectives for expanding the knowledge from data, which can capture highly complex nonlinear relationships between multivariate inputs and outputs. This work applied ML and DL methods to GCS-related studies at pore and reservoir scales, including digital rock physics (DRP) and the well-logging data interpretation and analysis. DRP provides cost-saving and practical core analysis methods, combining high-resolution imaging techniques, such as the three-dimensional (3D) X-ray computed tomography (CT) scanning, with advanced numerical simulations. Image segmentation is a crucial step of the DRP framework, affecting the accuracy of the following analyses and simulations. We proposed a DL-based workflow for boundary and small target segmentation in digital rock images, which aims to overcome the main challenge in X-ray CT image segmentation, partial volume blurring (PVB). The training data and the model architecture are critical factors affecting the performance of supervised learning models. We employed the entropy-based-masking indicator kriging (IK-EBM) to generate high-quality training data. The performance of IK-EBM on segmentation affected by PVB was compared with some commonly used image segmentation methods on the synthetic data with known ground truth. We then trained and tested the UNet++ model with nested architecture and redesigned skip connections. The evaluation metrics include the pixel-wise (i.e. F1 score, boundary-scaled accuracy, and pixel-by-pixel comparison) and physics-based (porosity, permeability, and CO2 blob curvature distributions) accuracies. We also visualized the feature maps and tested the model generalizations. Contact angle (CA) distribution quantifies the rock surface wettability, which regulates the multiphase behaviors in the porous media. We developed a DL-based CA measurement workflow by integrating an unsupervised learning pipeline for image segmentation and an open-source CA measurement tool. The image segmentation pipeline includes the model training of a CNN-based unsupervised DL model, which is constrained by feature similarity and spatial continuity. In addition, the over-segmentation strategy was adopted for model training, and the post-processing was implemented to cluster the model output to the user-desired target. The performance of the proposed pipeline was evaluated using synthetic data with known ground truth regarding the pixel-wise and physics-based evaluation metrics. The resulting CA measurements with the segmentation results as input data were validated using manual CA measurements. The GCS projects in the Illinois Basin are the first large-scale injection into saline aquifers and employed the latest pulsed neutron tool, the pulsed neutron eXtreme (PNX), to monitor the injected CO2 saturation. The well-logging data provide valuable references for the formation evaluation and CO2 monitoring in GCS in saline aquifers at the reservoir scale. In addition, data-driven models based on supervised ML and DL algorithms provide a novel perspective for well-logging data analysis and interpretation. We applied two commonly used ML and DL algorithms, support vector machine regression (SVR) and artificial neural network (ANN), to the well-logging dataset from GCS projects in the Illinois Basin. The dataset includes the conventional well-logging data for mineralogy and porosity interpretation and PNX data for CO2 saturation estimation. The model performance was evaluated using the root mean square error (RMSE) and R2 score between model-predicted and true values. The results showed that all the ML and DL models achieved excellent accuracies and high efficiency. In addition, we ranked the feature importance of PNX data in the CO2 saturation estimation models using the permutation importance algorithm, and the formation sigma, pressure, and temperature are the three most significant factors in CO2 saturation estimation models. The major challenge for the CO2 storage field projects is the large-scale real-time data processing, including the pore-scale core and reservoir-scale well-logging data. Compared with the traditional data processing methods, ML and DL methods achieved accuracy and efficiency simultaneously. This work developed ML and DL-based workflows and models for X-ray CT image segmentation and well-logging data interpretations based on the available datasets. The performance of data-driven surrogate models has been validated regarding comprehensive evaluation metrics. The findings fill the knowledge gap regarding formation evaluation and fluid behavior simulation across multiple scales, ensuring sequestration security and effect. In addition, the developed ML and DL workflows and models provide efficient and reliable tools for massive GCS-related data processing, which can be widely used in future GCS projects. / Doctor of Philosophy / Geological carbon sequestration (GCS) is the solution to ease the tension between the increasing carbon dioxide (CO2) concentrations in the atmosphere and the high dependence of human society on fossil energy. The sequestration requires the injection formation to have adequate storage capability, injectivity, and impermeable caprock overlain. Also, the injected CO2 plumes should be monitored in real-time to prevent any migration of CO2 to the surface. Therefore, pre-injection formation characterization and post-injection CO2 saturation monitoring are two critical and challenging tasks to guarantee the sequestration effect and security, which can be accomplished using the combination of pore-scale core analyses and reservoir-scale well-logging technologies. This work applied machine learning (ML) and deep learning (DL) methods to GCS-related studies across multiple spatial scales. We developed supervised and unsupervised DL-based workflows to segment the X-ray computed-tomography (CT) image of digital rocks for the pore-scale studies. Image segmentation is a crucial step in the digital rock physics (DRP) framework, and the following analyses and simulations are conducted on the segmented images. We also developed ML and DL models for well-logging data interpretation to analyze the mineralogy and estimate CO2 saturation. Compared with the traditional well-logging analysis methods, which are usually time-consuming and prior knowledge-dependent, the ML and DL methods achieved comparable accuracy and much shorter processing time. The performance of developed workflows and models was validated regarding comprehensive evaluation metrics, achieving excellent accuracies and high efficiency simultaneously. We are at the early stage of CO2 sequestration, and relevant knowledge and tools are inadequate. In addition, the main challenge of CO2 sequestration field projects is the large-scale and real-time data processing for fast decision-making. The findings of this dissertation fill the knowledge gap in GCS-related formation evaluation and fluid behavior simulations across multiple spatial scales. The developed ML and DL workflows provide efficient and reliable tools for massive data processing, which can be widely used in future GCS projects.

<p>Kompjuterizovana tomografija (CT) je u poslednje vreme ušla na velika vrata sa razvojem industrijskih CT sistema, usled njene primene u različitim oblastima, a uveliko ulazi i u polje koodinatne metrologije. Zbog karakterizacije objekata sačinjenih od različitih materijala (najčešće metala i plastike), javljaju se određeni problem u vidu nastanka artefakata kod rezultata dimenzionalnih merenja. Istraživanja koja su sprovedena u ovoj doktorskoj disertaciji se bave problemom redukcije uticaja tih artefakata i segmentacije 2D snimaka. Razvijen je novi model koji je baziran na primeni hibridne metode gde je izvršena kombinacija dve metode za obradu slike, a to su fazi klasterizacija i rast regiona. Aksenat je stavljen na primeni ove hibridne metode radi dobijanja tačnijih rezultata segmentacije, što direktno utiče i na rekonstrukciju dimenzionalno tačnijih 3D modela.</p> / <p>Computed tomography (CT) has recently entered a large door with the development of industrial CT systems, due to its application in many different areas, and it is already entering the field of coordinate metrology. Due to its ability to non-destructively characterize objects made of different materials (typicaly metals and plastics), a certain problem arises in the form of artefacts that are present in the results. Research carried out in this dissertation deals with the problem of reducing the impact of these artefacts and the 2D image segmentation. A new model was developed based on the application of the hybrid method where a combination of two methods for image processing was performed, which are fuzzy clustering and region growing. The accent is emphasized in the application of this hybrid method in order to obtain more accurate segmentation results, which directly affects the reconstruction of dimensionally more accurate 3D models.</p>

Dual-energy computed tomography (DECT) has shown great potential in the reduction of uncertainties of proton ranges and low energy photon cross section estimation used in radiation therapy planning. The work presented herein investigated three contributions for advancing DECT applications. 1) A linear and separable two-parameter DECT, the basis vector model (BVM) was used to estimate proton stopping power. Compared to other nonlinear two-parameter models in the literature, the BVM model shows a comparable accuracy achieved for typical human tissues. This model outperforms other nonlinear models in estimations of linear attenuation coefficients. This is the first study to clearly illustrate the advantages of linear model not only in accurately mapping radiological quantities for radiation therapy, but also in providing a unique model for accurate linear forward projection modelling, which is needed by the statistical iterative reconstruction (SIR) and other advanced DECT reconstruction algorithms. 2) Accurate DECT requires knowledge of x-ray beam properties. Using the Birch-Marshall1 model and beam hardening correction coefficients encoded in a CT scanner’s sinogram header files, an efficient and accurate way to estimate the x-ray spectrum is proposed. The merits of the proposed technique lie in requiring no physical transmission measurement after a one-time calibration against an independently measured spectrum. This technique can also be used in monitoring the aging of x-ray CT tubes. 3) An iterative filtered back projection with anatomical constraint (iFBP-AC) algorithm was also implemented on a digital phantom to evaluate its ability in mitigating beam hardening effects and supporting accurate material decomposition for in vivo imaging of photon cross section and proton stopping power. Compared to iFBP without constraints, both algorithms demonstrate high efficiency of convergence. For an idealized digital phantom, similar accuracy was observed under a noiseless situation. With clinically achievable noise level added to the sinograms, iFBP-AC greatly outperforms iFBP in prediction of photon linear attenuation at low energy, i.e., 28 keV. The estimated mean errors of iFBP and iFBP-AC for cortical bone are 1% and 0.7%, respectively; the standard deviations are 0.6% and 5%, respectively. The achieved accuracy of iFBP-AC shows robustness versus contrast level. Similar mean errors are maintained for muscle tissue. The standard deviation achieved by iFBP-AC is 1.2%. In contrast, the standard deviation yielded by iFBP is about 20.2%. The algorithm of iFBP-AC shows potential application of quantitative measurement of DECT. The contributions in this thesis aim to improve the clinical performance of DECT.

The overall objective of this study was to develop and characterise new normoxic polymer gel formulations for evaluation of complex 3-D treatment volumes for application in radiotherapy dosimetry. Throughout this thesis, the essential characteristics of normoxic polymer gels have been extensively investigated. Studies were performed on the chemical components of the MAGIC gel and an improved formulation was proposed. Various anti-oxidants were studied and different versions of the MAGIC gel with fewer chemicals were developed and named MAGAS and MAGAT gel dosimeters. The ascorbic acid anti-oxidant was found to have a slow oxygen scavenging rate and therefore a delay period between manufacture and irradiation of the MAGAS gel was necessary before the gel became radiation sensitive. Vacuum pumping on the MAGAS gel solution to remove dissolved oxygen was shown to initially increase the R2-dose response and sensitivity of the dosimeter, reducing the time between manufacture and irradiation. Studies of the MAGAS gel for measurement of depth dose showed that MAGAS gel has potential as a clinical radiotherapy dosimetry tool. The radiological properties of MAGIC, MAGAS and MAGAT gels were investigated. Due to their high gelatine and monomer concentration, differences with water were observed for the cross-section ratios for attenuation, energy absorption and collision stopping power coefficient ratios through the therapeutic energy range. It was determined that when using and developing normoxic polymer gels the most important consideration for radiological water equivalence are the mass and relative electron densities. A preliminary study was performed with the hypoxic PAG gel dosimeter combined with tetrakis (hydroxymethyl) phosphonium chloride anti-oxidant to form a normoxic PAG gel dosimeter named PAGAT gel. It was found PAGAT gel compared favourably with previous studies of the hypoxic PAG gel. An extensive study was subsequently undertaken in which PAGAT gel was investigated for a number of essential characteristics. The PAGAT gel formulation showed potential as a normoxic polymer gel for clinical radiotherapy dosimetry, which has a significantly reduced manufacturing time and procedure compared with the hypoxic PAG gel dosimeter. The radiological attenuation properties of the PAGAT and MAGAT gels were investigated as a feasibility study for using x-ray computerised tomography (CT) as an evaluation technique of normoxic polymer gels. CT was shown to have potential as an evaluation tool for measuring the dose response of normoxic polymer gel dosimeters. An investigation was performed on the CT diagnostic dose response of normoxic polymer gels. Normoxic polymer gels were found to have potential for use as a specialised tool in measuring computerised tomography dose index (CTDI) for acceptance testing and quality assurance of CT scanners in diagnostic radiology. These findings provide a significant contribution toward the development and successful implementation of normoxic polymer gel dosimetry to clinical radiotherapy.

Se zvyšující se dostupností medicínského CT vyšetření a s rostoucím počtem patologických stavů, pro které je indikováno, se redukce pacientské dávky ionizujícího záření stává stále aktuálnějším tématem. Výrazný pokrok v tomto odvětví představují nové metody rekonstrukce obrazů z projekcí, tzv. moderní iterativní rekonstrukční metody. Zároveň se zavedením těchto metod vzrostla potřeba pro měření obrazové kvality. Kvalita iterativně rekonstruovaných dat byla doposud kvantitativně hodnocena pouze na fantomových datech nebo na malých oblastech zájmu v reálných pacientských datech. Charakter iterativně rekonstruovaných dat však naznačuje, že tyto přístupy nadále nejsou dostatečné a je nutné je nahradit přístupy novými. Hlavním cílem této dizertační práce je navrhnout nové přístupy k měření kvality CT obrazových dat, které budou respektovat specifika iterativně rekonstruovaných obrazů a budou počítána plně automaticky přímo z reálných pacientských dat.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第18806号 / 理博第4064号 / 新制||理||1585(附属図書館) / 31757 / 京都大学大学院理学研究科地球惑星科学専攻 / (主査)教授 平島 崇男, 教授 大沢 信二, 准教授 河上 哲生 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21737号 / 工博第4554号 / 新制||工||1710(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 木村 亮, 准教授 肥後 陽介, 准教授 木元 小百合 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

[Truncated abstract] Rocks, as the host to natural chains of coupled thermal, hydraulic and mechanical processes, are heterogeneous at a variety of length scales, and in their mechanical properties, as well as in the hydraulic and thermal transport properties. Rock heterogeneity affects the ultimate hydro-carbon recovery or geothermal energy production. This heterogeneity has been considered one important and difficult problem that needs to be taken into account for its effect on the coupled processes. The aim of this thesis is to investigate the effect of rock heterogeneity on multi-physical processes. A fully coupled finite element model, hereinafter referred to as a porosity-based model (PBM) was developed to characterise the thermal-hydraulic-mechanical (THM) coupling processes. The development of the PBM consists of a two-staged workflow. First, based on poromechanics, porosity, one of the inherent rock properties, was derived as a variant function of the thermal, hydraulic and mechanical effects. Then, empirical relations or experimental results, correlating porosity with the mechanical, hydraulic and thermal properties, were incorporated as the coupling effects. In the PBM, the bulk volume of the model is assumed to be changeable. The rate of the volumetric strain was derived as the difference of two parts: the first part is the change in volume per unit of volume and per unit of time (this part was traditionally considered the rate of volumetric strain); and the second is the product of the first part and the volumetric strain. The second part makes the PBM a significant advancement of the models reported in the literature. ... impact of the rock heterogeneity on the hydro-mechanical responses because of the requirement of large memory and long central processing unit (CPU) time for the 3D applications. In the 2D PBM applications, as the thermal boundary condition applied to the rock samples containing some fractures, the pore pressure is generated by the thermal gradient. Some pore pressure islands can be generated as the statistical model and the digital image model are applied to characterise the initial porosity distribution. However, by using the homogeneous model, this phenomenon cannot be produced. In the 3D PBM applications, the existing fractures become the preferential paths for the fluid flowing inside the numerical model. The numerical results show that the PBM is sufficiently reliable to account for the rock mineral distribution in the hydro-mechanical coupling processes. The applications of the statistical method and the digital image processing technique make it possible to visualise the rock heterogeneity effect on the pore pressure distribution and the heat dissipation inside the rock model. Monitoring the fluid flux demonstrates the impact of the rock heterogeneity on the fluid product, which concerns petroleum engineering. The overall fluid flux (OFF) is mostly overestimated when the rock and fluid properties are assumed to be homogeneous. The 3D PBM application is an example. As the rock is heterogeneous, the OFF by the digital core is almost the same as that by the homogeneous model (this is due to that some fractures running through the digital core become the preferential path for the fluid flow), and around 1.5 times of that by the statistical model.