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We study surface deformation monitoring over a hydrocarbon reservoir in the Middle East with permanent scatterer interferometric synthetic aperture radar (PSInSAR). By combining data from two different observation angles, it is possible to disentangle horizontal and vertical deformation of the Earth's surface. We benchmark the PSInSAR data against an existing GPS network and find good agreement in both vertical and horizontal displacements. In order to relate the surface data to the reservoir, we invert the surface deformations using an analytical geomechanical model and obtain reservoir strain. Assuming linear poroelasticity, we relate the strain to pressure depletion. The areal extent of the reservoir strain is in good agreement with predictions from reservoir simulation. By considering velocity gradient maps, we find intriguing relationships between major faults in the reservoir and the surface data. We conclude that surface deformation monitoring and geomechanical inversion can provide valuable information on dynamic reservoir behaviour.

We have developed a prototype fluorescence microscope which, using tomographic image acquisition and reconstruction techniques, can automatically combine conventional and/or confocal image stacks taken at a number of orientations into a single, very-high-resolution 3D image. We use the term "microtomography" in a broad sense to denote digital image reconstruction from multiple imaging operations which are not necessarily projections. Our system holds a biological specimen inside a thin capillary tube which is rotatable over a 360 degree range beneath an immersion objective. 3D fluorescence image data volumes are acquired by frame-grabbing a through-focus series of 2D images at each angle of rotation. Digital reconstruction of the multiangle data volumes produces a single very-high-resolution 3D image and involves algorithms which perform rotation, interpolation, alignment and normalization operations in frequency (Fourier) space.

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We addressed the problem of the well-to-seismic tie as a Bayesian inversion for the wavelet and well path in the impedance domain. The result of the joint inversion is a set of wavelets for multiple angle stacks, and a corresponding well path. The wavelet optimally links the impedance data along the well to the seismic data along the optimized well path in the seismic time domain. Starting with prior distribution for wavelet and well path, the method calculates the posterior distribution of conditioning the prior distributions with the seismic and well-log data. This is done by iteratively inverting the seismic data with the current wavelet, to obtain an impedance cube around the well. In a second step, the seismic impedances are projected onto the well path. By minimizing the misfit between the inverted seismic impedances and the impedances derived from the well log, the wavelet and well path are optimized. Comparing the well and seismic data in the impedance domain enables the method to work on short and noisy well logs. Another advantage of this method is its ability to derive wavelets for multiple angle stacks and multiple well locations simultaneously. We tested the method on synthetic and real data examples. The algorithm performed well in the synthetic examples, in which we had control over the modeling wavelet, and the wavelets derived for a real data example showed consistently good seismic-to-well ties for six angle stacks and seven wells. The main algorithm we developed was aimed to linearize the problem. We compared the posterior distribution of the linearized result with a sampling-based result in a real data example and found good agreement.