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Viscoelastic seismic parameters are expressions of underlying petrophysical properties. Theoretical and empirically derived petrophysical/seismic relations exist, but each is limited in the number and the range of values of the variables used. To provide a more comprehensive empirical model, we combined lab measurements from 18 published data sets and well log data for sandstone samples, and determined least‐squares coefficients across them all. The dependent variables are the seismic parameters of bulk density (ρ), compressional and shear wave velocities ([Formula: see text] and [Formula: see text]), and compressional and shear wave quality factors ([Formula: see text] and [Formula: see text]). The independent variables are effective pressure, porosity, clay content, water saturation, permeability, and frequency. As the derived expressions are empirical correlations, no causal relations should be inferred. Prediction of ρ is based on volumetric mixing of the constituents. For [Formula: see text] and [Formula: see text] predictions, separate sets of coefficients are fitted for three water saturation conditions: dry, partially saturated, and fully saturated. Predictions of [Formula: see text] and [Formula: see text] are fitted as functions of porosity, clay content, effective pressure, saturation, and frequency. Predictions of [Formula: see text] are fitted as a function of porosity, clay content, permeability, saturation, frequency, and pressure. Interactions between effective pressure, saturation, and frequency are included. Predictions of [Formula: see text] are obtained from [Formula: see text] and [Formula: see text]. The result is a composite model that is more comprehensive than previous models and that predicts seismic properties from the petrophysical properties. Empirically estimated values of ρ, [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] for the composite data over all saturations predict the measurements with correlation coefficients [Formula: see text] that range from a low of 0.65 (for [Formula: see text],) to a high of 0.90 (for [Formula: see text]). As the fitted relations have been derived from data with limited parameter ranges, extrapolation is not advised, and they are not intended to substitute for locally derived relations based on site‐specific data. Nevertheless, the derived expressions produce representative values that will be useful when approximate, internally consistent predictions are sufficient. Potential future applications include building of seismic reservoir models from petrophysical data and analysis of the sensitivity of seismic data to changes in reservoir properties.

The cost and complexity of deep-water subsalt development wells is so great that a very limited spatial sampling of the target reservoir is achievable with well data. Thus, the quantitative use of seismic data becomes of paramount importance. Poststack seismic amplitude inversion, and poststack seismic attribute analysis and modeling are frequently employed to perform quantitative prediction of reservoir properties from surface seismic data. Several authors have shown that both absolute and relative acoustic impedance (AAI and RAI, respectively) derived from poststack seismic amplitude inversion can be useful for quantitative estimates of summary reservoir properties such as average porosity, net-to-gross, and others. This includes suprasalt and minibasin clastic reservoirs typically encountered in the Middle and Lower Tertiary plays in the deep-water Gulf of Mexico (several of which are also encountered subsalt), where depth to target can exceed 9000 m and highest frequencies at target are often rather low (20–25 Hz) (Bogan et al., 2003, Vernik et al., 2002).

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Empirical relations are found between measured petrophysical/petrologic, seismic, and electrical properties of sandstone and carbonate samples by least‐squares fitting at room pressure and ambient saturation. The measured parameters include porosity (φ), fluid permeability (k), clay content (C), grain density (ρg), bulk density (ρb), P‐wave velocity (Vp), electrical conductivity (σ), and dielectric constant (κ). The samples are from reservoir analog sites in the Ferron Sandstone in central Utah and the Ellenburger carbonate in central Texas. Crossplots and regression analysis are done separately for the sandstone and the carbonate samples. For the sandstone samples, predictions with correlation coefficients (R2) greater than 0.75 include ρb from φ, ln k from φ and C, κ from ln k and φ, κ from ρb and C, and κ from ln k. Predictions with 0.65 < R2 < 0.75 include κ from Vp and ρb, κ from Vp and φ, Vp from ln k and C, κ from ln φ, and κ from ρb. In general, σ is difficult to predict, with the best R2(0.48) obtained in a prediction of σ from ln k. Relationships for the carbonate samples are generally less reliable, which is attributed to a complex history of multiple phases of karsting and burial. The largest R2 values obtained are 0.67 for prediction of σ from κ, and 0.36 for prediction of σ from ρg. All the other R2 values are ≤0.19. Both the sandstone and the carbonate data show σ‐φ; relations with negative coefficients, rather than positive as predicted by Archie's law, because of the very low water saturations. In the sandstone, the water connectivity is reduced with increasing grain surface area (with increasing φ and k), so σ decreases. In the carbonate, σ correlates with the degree of dolomitization, and the water content is too low to contribute to σ.