Estimation of rock physics properties from seismic attributes — Part 1: Strategy and sensitivity analysis

Abstract

The quantitative estimation of rock physics properties is of great importance in any reservoir characterization. We have studied the sensitivity of such poroelastic rock physics properties to various seismic viscoelastic attributes (velocities, quality factors, and density). Because we considered a generalized dynamic poroelastic model, our analysis was applicable to most kinds of rocks over a wide range of frequencies. The viscoelastic attributes computed by poroelastic forward modeling were used as input to a semiglobal optimization inversion code to estimate poroelastic properties (porosity, solid frame moduli, fluid phase properties, and saturation). The sensitivity studies that we used showed that it was best to consider an inversion system with enough input data to obtain accurate estimates. However, simultaneous inversion for the whole set of poroelastic parameters was problematic due to the large number of parameters and their trade-off. Consequently, we restricted the sensitivity tests to the estimation of specific poroelastic parameters by making appropriate assumptions on the fluid content and/or solid phases. Realistic a priori assumptions were made by using well data or regional geology knowledge. We found that (1) the estimation of frame properties was accurate as long as sufficient input data were available, (2) the estimation of permeability or fluid saturation depended strongly on the use of attenuation data, and (3) the fluid bulk modulus can be accurately inverted, whereas other fluid properties have a low sensitivity. Introducing errors in a priori rock physics properties linearly shifted the estimations, but not dramatically. Finally, an uncertainty analysis on seismic input data determined that, even if the inversion was reliable, the addition of more input data may be required to obtain accurate estimations if input data were erroneous.

Estimation of rock physics properties from seismic attributes — Part 2: Applications

“…We choose to use the generic model defined by Pride (2005), which combined classical Biot-Gassmann theories with a generalized dynamic permeability (Johnson et al, 1987). The full rock physics model is described by Dupuy et al (2016b). The complex slownesses of P-and S-waves (s P and s S ) are given by…”

“…In this paper, after a quick reminder about the method defined in Dupuy et al (2016b), we apply the process to synthetic and field data cases. All applications deal with monitoring cases, which allow to reduce the uncertainty on a priori rock physics properties and to take advantage of the time-lapse data redundancy to focus on the fluid changes estimates.…”

“…on V P and V S are similar, but fluids have different effects on V P and V S (Berryman et al, 2002;Avseth et al, 2005). 3) The sensitivity tests carried out by Dupuy et al (2016b) show that using more than one input data helps to constrain the poroelastic inversion. In addition, parameters such as saturation and permeability cannot be estimated without attenuation input data.…”

“…The poroelastic FWI-like method is very difficult to implement because of the computational cost and the large trade-off between poroelastic properties (De Barros and Dietrich, 2008;Morency et al, 2009;De Barros et al, 2010). Consequently, we choose to use a two-step inverse approach: (1) from viscoelastic seismic attributes (velocities and quality factors) determined by a first inversion step and (2) we recover rock physics parameters by an inversion process, which is described by Dupuy et al (2016b).…”

“…In addition to the use of a Biot dynamic poroelasticity theory (Dupuy et al, 2016b), our work relies on the use of high-resolution quantitative data, derived with FWI techniques. Inverted seismic attributes are generally limited to P-and S-wave velocities, extracted from seismograms using arrival-time tomography.…”