Time-lapse seismic imaging by linearized joint inversion

by Gboyega Ayeni

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Abstract This dissertation presents methods that overcome some limitations in the application of time-lapse seismic imaging to subsurface reservoir monitoring. These methods attenuate artifacts and distortions in time-lapse seismic images that are caused by differences in survey acquisition geometries, presence of obstructions, complex overburden and man-made noise. Unless these artifacts are attenuated, it is impossible to make reliable deductions about changes in subsurface reservoir properties from time-lapse seismic images.

Improvements to two conventional post-imaging seismic cross-equalization methods are considered. Multidimensional warping of baseline and monitor images is implemented as sequential one-dimensional cross-correlations and interpolations. This method avoids the cost of full three-dimensional warping, and it avoids errors caused by considering only vertical apparent displacements between images. After warping, matched filters are derived using optimal parameters derived using an Evolutionary Programming algorithm. Applications to four North Sea data sets show that a combination of these two methods provides an efficient and robust cross-equalization scheme. Importantly, the warping method is a key preprocessing tool for linearized joint inversion.

Linearized joint inversion of time-lapse data sets is an extension of least-squares migration/inversion of seismic data sets. Linearized inversion improves both structural and amplitude information in seismic images. Joint inversion allows incorporation spatial and temporal regularizations/constraints, which stabilize the inversion and ensure that results are geologically plausible. Implementations of regularized joint inversion in both the data-domain and image-domain are considered. Joint data-domain inversion minimizes a global least-squares objective function, whereas joint image-domain inversion utilizes combinations of target-oriented approximations of the Hessian of the least-squares objective function. Applications to synthetic data sets show that, compared to migration or separate inversion, linearized joint inversion provides time-lapse seismic images that are less sensitive to geometry differences between surveys and to the overburden complexity. An important advantage of an image-domain inversion is that it can be solved efficiently for a small target around the reservoir.

Joint image-domain inversion requires careful preprocessing to ensure that the data contain only primary reflections, and that the migrated images from different vintages are aligned. The importances of various preprocessing steps are demonstrated using two-dimensional time-lapse data subsets from the Norne field. Applications of regularized image-domain joint inversion to the Valhall Life-of-Field Seismic (LoFS) data sets show that it provides improved time-lapse images compared to migration. These applications show that regularized joint image-domain inversion attenuates obstruction artifacts in time-lapse seismic images and that it can be used to image several data sets simultaneously. Furthermore, because it is computationally efficient, joint image-domain inversion can be repeated quickly using various a priori information.

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