A plethora of seismic attributes are currently in use for reservoir prediction and characterization. Some are useful in understanding subsurface stratigraphy (like channels) while others are useful for structural interpretation (anticlines, faults/fractures etc). Volumetric computation of seismic attributes is helpful to interpreters for 3D seismic visualization and interpretation. In addition to the time of a seismic reflection (resulting in a time-structure map), the more commonly used seismic attributes are rms amplitude, frequency, coherence, AVO, and impedance. These attributes are based on clearly established morphological or petrophysical models with some attributes sensitive to rock types, some to fluid saturation, some to porosity and some to minor faults or fractures within the reservoir. Our case study of a diagenetically altered Mississippian limestone resulting in complex paleo topography defies simple analysis. Even with adequate well control and good ties to 3D seismic data, uncertainty in the attribute expression of different reservoir architecture may result in the failure to identify sweet spots for drilling.
In recent years, 3D volumetric attributes have gained wide acceptance by seismic interpreters. The early introduction of the single-trace complex trace attribute was quickly followed by seismic sequence attribute mapping workflows. Three-dimensional geometric attributes such as coherence and curvature are also widely used. Most of these attributes correspond to very simple, easy-to-understand measures of a waveform or surface morphology. However, not all geologic features can be so easily quantified. For this reason, simple statistical measures of the seismic waveform such as rms amplitude and texture analysis techniques prove to be quite valuable in delineating more chaotic stratigraphy. In this paper, we coupled structure-oriented texture analysis based on the gray-level co-occurrence matrix with self-organizing maps clustering technology and applied it to classify seismic textures. By this way, we expect that our workflow should be more sensitive to lateral changes, rather than vertical changes, in reflectivity. We applied the methodology to a remote sensing image and to a 3D seismic survey acquired over Osage County, Oklahoma, USA. Our results indicate that our method can be used to delineate meandering channels as well as to characterize chert reservoirs.
One of the major challenges of unconventional shale reservoirs is to understand the effects of organic richness (total organic carbon, TOC), mineralogy, microcracks, pore shape, and effective stress on elastic properties. The generation of petrophysical parameters, such as TOC, and quantification of total and organic porosities through a physically consistent petrophysical model are described. Rock-matrix density, which is a key parameter in determining total porosity, is estimated as a function of the amount of TOC and its level of maturity. Then the petrophysical parameters are used as inputs for rock-physics modeling to constrain the beddingnormal compressional-wave velocity as a function of various parameters (e.g., TOC, porosity, mineralogy, pore shapes, and microcrack density) in combination with effective stress. Modeling results on three shale plays from North America show that compressional-wave velocity in these specific formations is controlled mainly by variations in TOC, mineralogy, and pore shape. Shear-wave velocity in organic shales also was refined as a function of compressional-wave velocity and amount of TOC.
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