Geo-consistent depth trends: Honoring geology in siliciclastic rock-physics depth trends

2019 ◽  
Vol 38 (5) ◽  
pp. 379-384 ◽  
Author(s):  
Anders Dræge
Keyword(s):  
Seismic Response ◽  
Standard Method ◽  
Rock Physics ◽  
New Method ◽  
Temperature History ◽  
New Approach ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Siliciclastic Rock ◽  
Well Data

A new method for modeling rock-physics depth trends called “geo-consistent depth trend modeling” is presented. No new rock-physics models are developed in this work, but existing models are put together in a new workflow. The workflow integrates rock-physics modeling with petrologic porosity models that account for burial, pressure, and temperature history. The new approach honors geologic trends, patterns, and cyclicity in the rocks. Examples based on well data are given to show how depositional trends can influence seismic response and depth trends. Geo-consistent depth trends are compared with the standard method for rock-physics depth trends, and differences are discussed. Geo-consistent depth trends can contribute to increased understanding of the subsurface and give input to risking of targets in exploration.

Bayesian linearized rock-physics inversion

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. D625-D641 ◽  
Author(s):  
Dario Grana
Keyword(s):  
Granular Media ◽  
Computational Cost ◽  
Rock Physics ◽  
Real Data ◽  
Data Sets ◽  
New Approach ◽  
Amplitude Variation With Offset ◽  
Fluid Properties ◽  
Rock Physics Modeling ◽  
Physics Modeling

The estimation of rock and fluid properties from seismic attributes is an inverse problem. Rock-physics modeling provides physical relations to link elastic and petrophysical variables. Most of these models are nonlinear; therefore, the inversion generally requires complex iterative optimization algorithms to estimate the reservoir model of petrophysical properties. We have developed a new approach based on the linearization of the rock-physics forward model using first-order Taylor series approximations. The mathematical method adopted for the inversion is the Bayesian approach previously applied successfully to amplitude variation with offset linearized inversion. We developed the analytical formulation of the linearized rock-physics relations for three different models: empirical, granular media, and inclusion models, and we derived the formulation of the Bayesian rock-physics inversion under Gaussian assumptions for the prior distribution of the model. The application of the inversion to real data sets delivers accurate results. The main advantage of this method is the small computational cost due to the analytical solution given by the linearization and the Bayesian Gaussian approach.

Rock-physics diagnostics of an offshore gas field

Geophysics ◽  
2017 ◽  
Vol 82 (4) ◽  
pp. MR121-MR132 ◽  
Author(s):  
Uri Wollner ◽  
Yunfei Yang ◽  
Jack P. Dvorkin
Keyword(s):  
Rock Physics ◽  
Rock Properties ◽  
Gas Field ◽  
Amplitude Variation With Offset ◽  
Rock Physics Modeling ◽  
Physics Modeling ◽  
Using Data ◽  
Physics Model ◽  
Well Data ◽  
Rock Physics Model

Seismic reflections depend on the contrasts of the elastic properties of the subsurface and their 3D geometry. As a result, interpreting seismic data for petrophysical rock properties requires a theoretical rock-physics model that links the seismic response to a rock’s velocity and density. Such a model is based on controlled experiments in which the petrophysical and elastic rock properties are measured on the same samples, such as in the wellbore. Using data from three wells drilled through a clastic offshore gas reservoir, we establish a theoretical rock-physics model that quantitatively explains these data. The modeling is based on the assumption that only three minerals are present: quartz, clay, and feldspar. To have a single rock-physics transform to quantify the well data in the entire intervals under examination in all three wells, we introduced field-specific elastic moduli for the clay. We then used the model to correct the measured shear-wave velocity because it appeared to be unreasonably low. The resulting model-derived Poisson’s ratio is much smaller than the measured ratio, especially in the reservoir. The associated synthetic amplitude variation with offset response appears to be consistent with the recorded seismic angle stacks. We have shown how rock-physics modeling not only helps us to correct the well data, but also allows us to go beyond the settings represented in the wells and quantify the seismic signatures of rock properties and conditions varying in a wider range using forward seismic modeling.

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