We present a new approach to structural interpretation of 3D seismic data with the objectives of simplifying the task and reducing the interpretation time. The essential element is the stepwise removal of noise, and eventually of small‐scale stratigraphic and structural features, to derive more and more simple representations of structural shape. Without noise and small‐scale structure, both man and machine (autotrackers) can arrive at a structural interpretation faster. If the interpreters so wish, they can refine such an initial crude structural interpretation in selected target areas. We discuss a class of filters that removes noise and, if desired, simplifies structural information in 3D seismic data. The gist of these filters is a smoothing operation parallel to the seismic reflections that does not operate beyond reflection terminations (faults). These filters therefore have three ingredients: (1) orientation analysis, (2) edge detection, and (3) edge‐preserving oriented smoothing. We discuss one particular implementation of this principle in some detail: a simulated anisotropic diffusion process (low‐pass filter) that diffuses the seismic amplitude while the diffusion tensor is computed from the local image structure (so that the diffusion is parallel to the reflections). Examples show the remarkable effects of this operation.
leads to deformation, compaction, displacements, and stress change in the reservoir as well as in the surrounding rock. Such stress changes affect the acoustic-wave velocity and bulk density. This has two implications. It changes the contrast in acoustic impedance between reservoir and overburden, resulting in seismic amplitude changes at the top of the compacting reservoir. Secondly, it changes the traveltime of seismic reflection waves, leading to arrival-time delays (time shifts) of seismic data gathered in the repeat survey compared to data gathered in the base survey (Hatchell et al, 2004; Kenter et al, 2004; Stammeijer et al, 2004). Maps of time shifts can then indicate the areal distribution of reservoir compaction, and thus reveal the areal distribution of depletion. This could help to locate bypassed oil in undrained compartments, identify drilling targets and sidetracks, and avoid expensive infill wells. These interesting geophysical applications of reservoir mechanics justify questions about how accurate such geomechanical models really are. What is their sensitivity to the (natural) variation in input parameters like geologic structure and sedimentological detail, depletion pattern, and mechanical property distribution? This question is closely related to our ability to capture this variation in computer models via upscaling. Analytical models based on simplified reservoir shapes and linear elasticity show that the reservoir-compaction-induced stress change in the overburden is governed by the contrast in mechanical properties of the reservoir and the rock surrounding it, and by the reservoir shape and size with respect to its depth of burial (Geertsma, 1973; Segall, 1992). Memory and computational capacity of computers now allow numerical analysis with fine-scale (tens to hundreds of meters) geologic reality in geomechanical models. This paper describes two computer simulations with finite-element analysis to investigate the influence of proximity and structure of halite rocksalt and stiff chalk on reservoir-compaction-induced stress change in the overburden. Salt and chalk are abundant in many hydrocarbon basins. Salt is of particular interest because of its ability to trap hydrocarbons and perturb the stress field in adjacent sediments, with implications for pore pressure and fracture gradient prediction, and for reservoir monitoring with time shifts. Chalk contains a large amount of world oil reserves and, with its productivity often influenced by fracture systems, understanding its stress development is important.Model A: stacked sands bounded by salt. The size of the 3D geomechanical model was 19 ǂ 13 km laterally and 12 km in depth, and it had 142 100 elements. Boundaries of 18 producing unconsolidated ("loose") sands were upscaled to seven layers with a total vertical thickness of about 400 m (Figure 1). The sands have a porosity of 25-32% of bulk volume. Sand-specific estimates of vertical uniaxial-strain bulk volume compressibility (C uv ) range from 4 ǂ 10 -4 MPa to 1.6 ǂ 10 -3 MPa, based ...
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