Doctor of Philosophy

dissertationNumerical simulation of the geometrically complex fractured reservoirs has been a major engineering challenge. The deficiencies of continuum models are often addressed using the discrete fracture network (DFN) models which represent the complex fracture geometry explicitly. The primary goal in this dissertation is to explore ways of applying the DFN methodology to solve a variety of multiphase problems in oil reservoir simulation. Three-dimensional, three-phase simulators using the control-volume finiteelement scheme were used. After completing validation and fracture-property sensitivity studies, the limitation of employing the often-used Oda homogenization method was shown followed by the development of a simpler geometric scheme. The important question of oil recovery from basement reservoirs (Type I) composed of fractures of various sizes was examined in detail. Oil recovery and breakthrough behavior of this system comprised of seismic and subseismic features were investigated for different oil distributions, permeability values, levels of heterogeneity and rate. In general having more oil distributed in smaller systems led to lower recovery and quicker breakthrough. Lower permeabilities in the subseismic features also led to lower recovery. The recovery at given pore volume of water injected was rate dependent in all of the scenarios explored, with the lower rate production leading to about 5% higher oil in place recovery. This phenomenon was consistent when viewed from the point of view of gravity number for each displacement. The mechanism of gravity-dominated oil recovery in two-phase applications was explored, and a "critical rate" concept for obtaining higher recoveries in gravity-dominated flow was developed A multiscale upscaling exercise was performed to match the oil recovery performance from a structured fault zone using a single feature with different sets of relative permeability curves. The effectiveness of using DFN simulations for reservoirs containing matrix and fractures (Type II) was shown using two different systems. It was shown that placing wells either in the fault zone or in the matrix can have significant impact on recovery and breakthrough behavior. It was also demonstrated that fracture networks bring apparent anisotropy, and water-flooding from one direction or the other may affect oil recovery. Fractured reservoir simulation is high-performance computing - data and file management, computation, visualization, etc. are integral components of this exercise. A workflow to facilitate creation of fracture networks, gridding and simulation, and visualization was developed. A fully integrated two-dimensional graphical user interface (java-based) was also built

An analysis of shock isolation characteristics of a head of a woodpecker and its application to a bionic helmet

The effect of a woodpecker’s head structure on shock isolation was investigated from a dynamic point of view. A simplified multi-degree-of-freedom model was set up to study shock isolation characteristics of a woodpecker’s head. The shock-isolation performance of this model was calculated and analyzed by changing the dynamic parameters. And it was evaluated by two indexes: the absolute acceleration of the skull bone and the relative displacement between the skull bone and the beak. A bionic helmet model subjoining the elastic damping layer and the cushion pad was presented. Calculating the three-dimensional shock response surfaces validated it

External modulation method for generating accurate linear optical FMCW

Frequency modulation continuous wave (FMCW) lasers are key components in modern optical imaging. However, current intracavity modulation lasers do not exhibit low-frequency jitter rate and high linearity due to the inherent relaxation oscillations. Although this may be compensated in a direct modulation laser diode using an optoelectronic feedback loop, the available sweep speed is moderately small. In this letter, a special external modulation method is developed to improve the performance of FMCW. Since only the first sideband optical field is used during the entire generation process, phase noise is kept to a minimum and is also independent of the sweep speed. We demonstrate that the linearity and jitter rates do not deteriorate appreciably when the sweep speed is changed over three orders of magnitude, even up to the highest sweep speed of 2.5 GHz/ μs

Panoramic Annular Localizer: Tackling the Variation Challenges of Outdoor Localization Using Panoramic Annular Images and Active Deep Descriptors

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.Comment: Accepted by ITSC 201

A New Multi-Atlas Based Deep Learning Segmentation Framework With Differentiable Atlas Feature Warping

Deep learning based multi-atlas segmentation (DL-MA) has achieved the state-of-the-art performance in many medical image segmentation tasks, e.g., brain parcellation. In DL-MA methods, atlas-target correspondence is the key for accurate segmentation. In most existing DL-MA methods, such correspondence is usually established using traditional or deep learning based registration methods at image level with no further feature level adaption. This could cause possible atlas-target feature inconsistency. As a result, the information from atlases often has limited positive and even counteractive impact on the final segmentation results. To tackle this issue, in this paper, we propose a new DL-MA framework, where a novel differentiable atlas feature warping module with a new smooth regularization term is presented to establish feature level atlas-target correspondence. Comparing with the existing DL-MA methods, in our framework, atlas features containing anatomical prior knowledge are more relevant to the target image feature, leading the final segmentation results to a high accuracy level. We evaluate our framework in the context of brain parcellation using two public MR brain image datasets: LPBA40 and NIREP-NA0. The experimental results demonstrate that our framework outperforms both traditional multi-atlas segmentation (MAS) and state-of-the-art DL-MA methods with statistical significance. Further ablation studies confirm the effectiveness of the proposed differentiable atlas feature warping module

Precise and label-free tumour cell recognition based on a black phosphorus nanoquenching platform

Breast cancer is a type of heterogeneous disease, which manifests as different molecular subtypes due to the complex nature of tumour initiation, progression, and metastasis. Accurate identification of a breast cancer subtype plays crucial roles in breast cancer management. Herein, taking advantage of the efficient quenching properties of black phosphorus nanosheets (BPNSs), in combination with the high specificity of ssDNA (or RNA) aptamer, a fluorometric duplexed assay that is capable of the simultaneous detection of two tumour markers within one run is developed. When mixed with BPNSs, the fluorescence of both FAM and Cy3 labelled aptamers was quenched. The presence of different subtypes of breast cancer cells restored the FAM and Cy3 fluorescence in distinct patterns according to their intrinsic features. The proposed assay can precisely recognise label-free breast cancer subtypes, providing an efficient method for cell type identification and guidance for subsequent breast cancer treatment. The significance of the proposed study is two-fold. First, we provide a simple method for sensitive and specific tumour cell detection; secondly, and more importantly, the proposed dual assay allows precise recognition of tumour cells and thus opens a door for rapid characterization and sorting of a wide range of tumours without using expensive instruments

Parallel, Multigrid Finite Element Simulator for Fractured/Faulted and Other Complex Reservoirs based on Common Component Architecture (CCA)

Black-oil, compositional and thermal simulators have been developed to address different physical processes in reservoir simulation. A number of different types of discretization methods have also been proposed to address issues related to representing the complex reservoir geometry. These methods are more significant for fractured reservoirs where the geometry can be particularly challenging. In this project, a general modular framework for reservoir simulation was developed, wherein the physical models were efficiently decoupled from the discretization methods. This made it possible to couple any discretization method with different physical models. Oil characterization methods are becoming increasingly sophisticated, and it is possible to construct geologically constrained models of faulted/fractured reservoirs. Discrete Fracture Network (DFN) simulation provides the option of performing multiphase calculations on spatially explicit, geologically feasible fracture sets. Multiphase DFN simulations of and sensitivity studies on a wide variety of fracture networks created using fracture creation/simulation programs was undertaken in the first part of this project. This involved creating interfaces to seamlessly convert the fracture characterization information into simulator input, grid the complex geometry, perform the simulations, and analyze and visualize results. Benchmarking and comparison with conventional simulators was also a component of this work. After demonstration of the fact that multiphase simulations can be carried out on complex fracture networks, quantitative effects of the heterogeneity of fracture properties were evaluated. Reservoirs are populated with fractures of several different scales and properties. A multiscale fracture modeling study was undertaken and the effects of heterogeneity and storage on water displacement dynamics in fractured basements were investigated. In gravity-dominated systems, more oil could be recovered at a given pore volume of injection at lower rates. However, if oil production can be continued at high water cuts, the discounted cumulative production usually favors higher production rates. The workflow developed during the project was also used to perform multiphase simulations in heterogeneous, fracture-matrix systems. Compositional and thermal-compositional simulators were developed for fractured reservoirs using the generalized framework. The thermal-compositional simulator was based on a novel 'equation-alignment' approach that helped choose the correct variables to solve depending on the number of phases present and the prescribed component partitioning. The simulators were used in steamflooding and in insitu combustion applications. The framework was constructed to be inherently parallel. The partitioning routines employed in the framework allowed generalized partitioning on highly complex fractured reservoirs and in instances when wells (incorporated in these models as line sources) were divided between two or more processors