This work studied the productivity evaluation and well test analysis of horizontal wells. The major components of this work consist of a 3D coupled reservoir/wellbore model, a productivity evaluation, a deconvolution technique, and a nonlinear regression technique improving horizontal well test interpretation. A 3D coupled reservoir/wellbore model was developed using the boundary element method for realistic description of the performance behavior of horizontal wells. The model is able to flexibly handle multiple types of inner and outer boundary conditions, and can accurately simulate transient tests and long-term production of horizontal wells. Thus, it can serve as a powerful tool in productivity evaluation and analysis of well tests for horizontal wells. Uncertainty of productivity prediction was preliminarily explored. It was demonstrated that the productivity estimates can be distributed in a broad range because of the uncertainties of reservoir/well parameters. A new deconvolution method based on a fast-Fourier-transform algorithm is presented. This new technique can denoise "noisy" pressure and rate data, and can deconvolve pressure drawdown and buildup test data distorted by wellbore storage. For cases with no rate measurements, a "blind" deconvolution method was developed to restore the pressure response free of wellbore storage distortion, and to detect the afterflow/unloading rate function using Fourier analysis of the observed pressure data. This new deconvolution method can unveil the early time behavior of a reservoir system masked by variable-wellbore-storage distortion, and thus provides a powerful tool to improve pressure transient test interpretation. The applicability of the method is demonstrated with a variety of synthetic and actual field cases for both oil and gas wells. A practical nonlinear regression technique for analysis of horizontal well testing is presented. This technique can provide accurate and reliable estimation of well-reservoir parameters if the downhole flow rate data are available. In the situation without flow rate measurement, reasonably reliable parameter estimation can be achieved by using the detected flow rate from blind deconvolution. It has the advantages of eliminating the need for estimation of the wellbore storage coefficient and providing reasonable estimates of effective wellbore length. This technique provides a practical tool for enhancement of horizontal well test interpretation, and its practical significance is illustrated by synthetic and actual field cases.

Both Gauss-Seidel Iterative 3D deconvolution and Richardson-Lucy like algorithms are used due to their stability and high quality results in high noise microscopic medical image processing. An approach to determine the difference between these two algorithms is presented in this paper. It is shown that the convergence rate and the quality of these two algorithms are influenced by the size of the point spread function (PSF). Larger PSF sizes causes faster convergence but this effect falls off for larger sizes . It is furthermore shown that the relaxation factor and the number of iterations are influencing the convergence rate of the two algorithms. It has been found that increasing relaxation factor and number of iterations improve convergence and can reduce the error of the deblurred image. It also found that overrelaxation converges faster than underrelaxation for small number of iterations. However, it can be achieved smaller final error with under-relaxation. The choice of underrelaxation factor and overrelaxation factor value are highly problem specific and different from one type of images. In addition, when it comes to 3D iterative deconvolution, the influence of boundary conditions for these two algorithms is discussed. Implementation aspects are discussed and it is concluded that cache memory is vital for achieving a fast implementation of iterative 3D deconvolution. A mix of the two algorithms have been developed and compared with the previously mentioned Gauss-Seidel and the Richardson-Lucy-like algorithms. The experiments indicate that, if the value of the relaxation parameter is optimized, then the Richardson-Lucy-like algorithm has the best performance for 3D iterative deconvolution. / Upplösningen på bilder tagna med mikroskop är idag begränsad av diffraktion. För att komma runt detta förbättras bilden digitalt utifrån en matematisk modell av den fysiska processen. Den här avhandlingen jämför två algoritmer för att lösa ekvationerna: Richardson-Lucy och Gauss-Seidel. Vidare studeras effekten av parametrar såsom utbredningen av ljusspridfunktionen och regularisering av ekvationslösaren. / Mobile: (0046)762778136

Miniature cameras play a key role in numerous imaging applications ranging from endoscopy and metrology inspection devices to smartphones and head-mount acquisition systems. However, due to the physical constraints, the imaging conditions, and the low quality of small optics, their imaging capabilities are limited in terms of the delivered resolution, the acquired depth of field, and the captured dynamic range. Computational imaging jointly addresses the imaging system and the reconstructing algorithms to bypass the traditional limits of optical systems and deliver better restorations for various applications. The scene is encoded into a set of efficient measurements which could then be computationally decoded to output a richer estimate of the scene as compared with the raw images captured by conventional imagers. In this dissertation, three task-based computational imaging techniques are developed to make low-quality miniature cameras capable of delivering realistic high-resolution reconstructions, providing full-focus imaging, and acquiring depth information for high dynamic range objects. For the superresolution task, a non-regularized direct superresolution algorithm is developed to achieve realistic restorations without being penalized by improper assumptions (e.g., optimizers, priors, and regularizers) made in the inverse problem. An adaptive frequency-based filtering scheme is introduced to upper bound the reconstruction errors while still producing more fine details as compared with previous methods under realistic imaging conditions. For the full-focus imaging task, a computational depth-based deconvolution technique is proposed to bring a scene captured by an ordinary fixed-focus camera to a full-focus based on a depth-variant point spread function prior. The ringing artifacts are suppressed on three levels: block tiling to eliminate boundary artifacts, adaptive reference maps to reduce ringing initiated by sharp edges, and block-wise deconvolution or depth-based masking to suppress artifacts initiated by neighboring depth-transition surfaces. Finally for the depth acquisition task, a multi-polarization fringe projection imaging technique is introduced to eliminate saturated points and enhance the fringe contrast by selecting the proper polarized channel measurements. The developed technique can be easily extended to include measurements captured under different exposure times to obtain more accurate shape rendering for very high dynamic range objects.

Two complementary solution strategies to the least-squares migration problem with sparseness- & continuity constraints are proposed. The applied formalism explores the sparseness of curvelets on the reflectivity and their invariance under the demigration migration operator. Sparseness is enhanced by (approximately) minimizing a (weighted) l1-norm on the curvelet coefficients. Continuity along imaged reflectors is brought out by minimizing the anisotropic difussion or total variation norm which penalizes variations along and in between reflectors. A brief sketch of the theory is provided as well as a number of synthetic examples. Technical details on the implementation of the optimization strategies are deferred to an accompanying paper: implementation.

Commonly the thermal maturity of the Late Devonian to Early Mississippian Woodford shale found on the flanks of the Arbuckle Mountains of Oklahoma is determined by vitrinite reflectance, values ranging from 0.3-1.5%. Using phyllosilicate minerals, specifically diagenetic mixed layer illite/smectite and diagenetic illite, an understanding of the extent and processes leading to the thermal maturation may be developed. Analysis by XRD of the clay mineralogy of the Woodford shale found kaolinite and mixed layer illite/smectite with <5% smectite and R≥3 stacking order. Modeling of the Woodford shale also suggests the percentage of smectite present in mixed layer illite/smectite to be <5% and commonly <2.5%. Deconvolution of the illite (001) peak supports the low smectite content and high illite crystallinity. The long range ordered illite, R≥3, and high illite crystallinity values are indicative of diagenesis to anchizone conditions suggesting a higher thermal maturity relative to previously measured values of vitrinite reflectance.

Axicons have been proposed for imaging applications since they offer the advantage of extended depth of field (DOF). This enhanced DOF comes at the cost of degraded image quality. Image processing has been proposed to improve the image quality. Initial efforts were focused on the use of an axicon in a borescope thereby extending depth of focus and eliminating the need for a focusing mechanism. Though promising, it is clear that image processing would lead to improved image quality. This would also eliminate the need, in certain applications, for a fiber optic imaging bundle as many modern day video borescopes use an imaging sensor coupled directly to the front end optics. In the present work, three types of refractive axicons are examined: a linear axicon, a logarithmic axicon and a Fresnel axicon. The linear axicon offers the advantage of simplicity and a significant amount of scientific literature including the application of image restoration techniques. The Fresnel axicon has the advantage of compactness and potential low cost of production. As no physical prior examples of the Fresnel axicons were available for experimentation until recently, very little literature exists. The logarithmic axicon has the advantage of nearly constant longitudinal intensity distribution and an aspheric design producing superior pre-processed images over the aforementioned elements. Point Spread Functions (PSFs) for each of these axicons have been measured. These PSFs form the basis for the design of digital image restoration filters. The performance of these three optical elements and a number of restoration techniques are demonstrated and compared.

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We present the modes identified for frequencies found in spectroscopic observations of the Doradus stars HD 189631, QW Puppis, and IR Draconis. A cross-correlation tech- nique was used to create mean line profiles for HD 189631. Four frequencies and modes were identified for this star: 1.6774±0.0002 d⁻¹, 1.4174±0.0002 d⁻¹, 0.0714±0.0002 d⁻¹, and 1.8228 ± 0.0002 d⁻¹ which were identified with the modes (l,m) = (1, +1), (1, +1), (2,−2), and (1, +1) respectively. A least-squares deconvolution method was implemented for line profile generation in the study of QW Puppis and IR Draconis. Three frequen- cies were identified for QW Puppis: 0.055972 ± 0.000004 d⁻¹, 0.064846 ± 0.000004, and 5.219398±0.000002 d⁻¹. These frequencies were identified with the modes (l,m) = (1,−1), (4,−1), (4, +1). Two frequencies were identified in spectra of the rapidly rotating star IR Draconis: 0.00515 ± 0.00003 d⁻¹ and 2.35538 ± 0.00004 d⁻¹; which were identified with (l,m) = (1,−1), and (1, +1) modes respectively. These mode identifications will assist in modelling the structure and interior conditions of these main sequence, non-radially pulsating stars.

The connectivity and structural integrity of the white matter of the brain is known to be implicated in a wide range of brain-related diseases and injuries. However, it is only since the advent of diffusion magnetic resonance imaging (dMRI) that researchers have been able to probe the miscrostructure of white matter in vivo. Presently, among a range of methods of dMRI, high angular resolution diffusion imaging (HARDI) is known to excel in its ability to provide reliable information about the local orientations of neural fasciculi (aka fibre tracts). It preserves the high angular resolution property of diffusion spectrum imaging (DSI) but requires less measurements. Meanwhile, as opposed to the more traditional diffusion tensor imaging (DTI), HARDI is capable of distinguishing the orientations of multiple fibres passing through a given spatial voxel. Unfortunately, the ability of HARDI to discriminate neural fibres that cross each other at acute angles is always limited. The limitation becomes the motivation to develop numerous post-processing tools, aiming at the improvement of the angular resolution of HARDI. Among such methods, spherical deconvolution (SD) is the one which attracts the most attentions. Due to its ill-posed nature, however, standard SD relies on a number of a priori assumptions needed to render its results unique and stable. In the present thesis, we introduce a novel approach to the problem of non-blind SD of HARDI signals, which does not only consider the existence of anisotropic diffusion component of HARDI signal but also explicitly take the isotropic diffusion component into account. As a result of that, in addition to reconstruction of fODFs, our algorithm can also yield a useful estimation of its related IDM, which quantifies a relative contribution of the isotropic diffusion component as well as its spatial pattern. Moreover, one of the principal contributions is to demonstrate the effectiveness of exploiting different prior models for regularization of the spatial-domain behaviours of the reconstructed fODFs and IDMs. Specifically, the fibre continuity model has been used to force the local maxima of the fODFs to vary consistently throughout the brain, whereas the bounded variation model has helped us to achieve piecewise smooth reconstruction of the IDMs. The proposed algorithm is formulated as a convex minimization problem, which admits a unique and stable minimizer. Moreover, using ADMM, we have been able to find the optimal solution via a sequence of simpler optimization problems, which are both computationally efficient and amenable to parallel computations. In a series of both in silico and in vivo experiments, we demonstrate how the proposed solution can be used to successfully overcome the effect of partial voluming, while preserving the spatial coherency of cerebral diffusion at moderate to severe noise levels. The performance of the proposed method is compared with that of several available alternatives, with the comparative results clearly supporting the viability and usefulness of our approach. Moreover, the results illustrate the power of applied spatial regularization terms.