Relative rock physics

Abstract

A quantitative framework for relative rock physics includes a definition of relative elastic property that is rooted in inverse theory and that can be used practically to compute the properties. The framework also includes a set of rules that quantify how operations on absolute properties affect their relative counterparts. From these rules, a comprehensive table of relative elastic properties can be generated, all of which are expressed in terms of relative P-velocity, S-velocity, and density. Finally, the framework includes empirical and model-based rock-physics rules that can be applied directly to the relative properties without reverting to their absolute counterparts. That provides a practical route to such interpretation techniques as direct-to-seismic fluid substitution. The framework offers a quantitative alternative to workflows centered on absolute seismic inversion. In some cases, relative rock-physics workflows are preferable to absolute flows because they avoid statistical complications associated with the nonstationarity of the mean of absolute elastic properties.

Using recursive inversion as input for gross-rock volume extraction from lithology prediction volumes: How bad can it be?

“…This could be corrected by using different PDFs for different depths. Another solution to this would be to apply a relative rock-physics approach (Ball et al, 2014). However, the use of absolute volumes compared to relative volumes is supported by the separation observed for the predicted hydrocarbon and water legs of a deep prospect (Figure 8).…”

A comparison of competing amplitude variation with offset techniques applied to tight gas sand exploration in the Cooper Basin of Australia

“…Using equation 1 above with strict adherence to the χ angles in Table 1 would be an example of deterministic AVO. Similarly, Ball et al (2014) mention that optimal relative inversions for different elastic properties vary with the average background for ðV S ∕V P Þ 2 . In Whitcombe et al (2002), equations 8 and 14 illustrate that different background trends between V P and density lead to different optimal weights in equation 1 above.…”

Reservoir properties prediction using extended elastic impedance: the case of Nianga field of West African Congo basin

“…Prior to determine the optimum angle for a particular target petrophysical log, each single ln EEI and petrophysical target logs were decomposed into the trend background and the relative components, following proposed economics techniques as in Ball et al (2014) and Hodrick and Prescott (1997). The correlation is then performed between two relative components.…”

“…Petrophysical logs such as porosity are generally trended, and the trends associated with ln EEI logs are function of the angle v. It is well documented that the unreal correlation coefficient may be the result of correlating two trended data (Ball et al 2014). Prior to determine the optimum angle for a particular target petrophysical log, each single ln EEI and petrophysical target logs were decomposed into the trend background and the relative components, following proposed economics techniques as in Ball et al (2014) and Hodrick and Prescott (1997).…”

“…Depending on the quality of well log data, one can expect seeing a perfect correlation between the tuned reflectivity and reservoir properties such as porosity, clay content, and water saturation. It is worth stating that factors like depth trend (Ball et al , 2014Thomas et al 2013), compaction trend (Avseth et al 2013) thickness, and lithology influence the quality of the correlation. Recently, Thomas et al (2013) have recommended the use of the logarithm of EEI (ln EEI) instead of the full EEI during the correlation analysis between reservoir properties and EEI logs to avoid statistical biases and loss of parity with reflection.…”