We describe a linear Bayesian inversion method to estimate the relevant petrophysical properties of the media forming a reflecting interface from the observations of amplitude variation with incidence angle. Three main steps characterize the proposed approach: – information from borehole logs are statistically analysed to estimate the empirical models that describe the functional relationship between petrophysical (e.g. porosity, saturation, pressure or depth) and seismic variable(P and S velocities and density); – the pure-mode (PP) reflection coefficient is parameterized in terms of the relevant petrophysical variables and is linearized in order to implement the linear inversion; – the sought petrophysical parameters are estimated from the seismic reflected amplitudes by applying the linearized inversion where a priori information, data and model errors and solutions are described by probability density functions. We test the method on synthetic and real data relative to reflections from a shale/gas-sand interface where the amplitude versus angle response, besides the lithological contrast, is mainly controlled by the saturation and porosity of the sand layer. The outcomes of the linearized inversion are almost identical to those obtained by a previously developed non-linear inversion method demonstrating the applicability of the linear inversion. It turns out that the gas-sand saturation in the range 0%–95% is a poorly resolved parameter while the porosity is the best resolved parameter. The issues of robustness and resolution of the inversion are discussed either through singular value decomposition analysis or the observation of the a posteriori probability density functions. The linear inversion algorithm, compared with the previously developed non-linear method, reduces significantly the computation time allowing for more extensive applications.
|Autori interni:||MAZZOTTI, ALFREDO|
|Autori:||CHIAPPA F; MAZZOTTI A|
|Titolo:||Estimation of petrophysical parameters by linearized inversion of angle domain pre-stack data|
|Anno del prodotto:||2009|
|Digital Object Identifier (DOI):||10.1111/j.1365-2478.2008.00742.x|
|Appare nelle tipologie:||1.1 Articolo in rivista|