Modeling the elastic properties of clay-bearing rocks (shales) requires thorough knowledge of the mineral constituents, their elastic properties, pore space microstructure, and orientations of clay platelets. Information about these variables and their complex interrelationships is rarely available for real rocks. We theoretically modeled the elastic properties of synthetic clay-water composites compacted in the laboratory, including estimates of pore space topology and percolation behavior. The mineralogy of the samples was known exactly, and the focus was on two monomineralic samples comprised of kaolinite and smectite. We used differential effective medium theory (DEM) and analysis of scanning electron microscope (SEM) images of the compacted kaolinite and smectite samples. Percolation behavior was included through calculations of critical porosities from measurements of the liquid limits of the individual clay powders. Quantitative analysis of the SEM images showed that the large scale (>0.1 μm) pore space of the smectite composite had more rounded pores (mean aspect ratio α ¼ 0.55) than the kaolinite composite (mean pore's aspect ratio α ¼ 0.44). However, models that used only these largescale pore shapes could not explain the compressional and shear velocity measurements. DEM simulations with a single pore aspect ratio showed that bulk and shear moduli are controlled by different pore shapes. Conversely, modeling results that combined critical porosity and dual porosity models into DEM theory compared well with the measured bulk and shear moduli of compacting kaolinite and smectite composites. The methods and results we used could be used to model unconsolidated clay-bearing rocks of more complex mineralogy.

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Predicting reservoir parameters, such as porosity, lithology, and saturations, from geophysical parameters is a problem with non‐unique solutions. The variance in solutions can be extensive, especially for saturation and lithology. However, the reservoir parameters will typically vary smoothly within certain zones—in vertical and horizontal directions. In this work, we integrate spatial correlations in the predicted parameters to constrain the range of predicted solutions from a particular type of inverse rock physics modelling method. Our analysis is based on well‐log data from the Glitne field, where vertical correlations with depth are expected. It was found that the reservoir parameters with the shortest depth correlation (lithology and saturation) provided the strongest constraint to the set of solutions. In addition, due to the interdependence between the reservoir parameters, constraining the predictions by the spatial correlation of one parameter also reduced the number of predictions of the other two parameters. Moreover, the use of additional constraints such as measured log data at specific depth locations can further narrow the range of solutions.

We evaluated a viscoelastic modeling of P-and S-wave velocity dispersion, attenuation, pressure, and fluid effects for a set of siliciclastic rock samples. Our analysis used a published laboratory data set of 63 sandstones with a wide range of compositional heterogeneities. We observed a notable correlation between the (velocity and attenuation) pressure sensitivity and the abundance/lack of quartz in the samples. We included compliant pores (low-aspect ratio) proportionally to the content of secondary minerals to account for the differential sensitivity to pressure. The observed velocity and attenuation were well reproduced by the applied viscoelastic modeling. We found that pores of significantly different scale required pore fluid relaxation time constants of proportionally different magnitudes to reproduce the velocity and attenuation measurements. The relaxation time constant of crack-sized pores can be one order of magnitude smaller than the constant of mesopores. Moreover, the velocity dispersion and attenuation signatures revealed that a pore textural model dependent on lithological composition is critical in the prediction of time-lapse fluid and pressure responses.

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