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In this work, we focus on a Bayesian inversion method for the estimation of reservoir properties from seismic data and we study how the inversion parameters, such as rock-physics and geostatistical parameters, can affect the inversion results in terms of reservoir performance quantities (pore volume and connectivity). We apply a Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of facies, acoustic impedance and porosity. The method is based on a Gibbs algorithm integrated with geostatistical methods that sample spatially correlated subsurface models from the posterior distribution. With the ensemble of multiples scenarios of the subsurface conditioned to the experimental data, we can evaluate two quantities that impact the production of the reservoir: the reservoir connectivity and the connected pore volume. For each set of parameters, the inversion method yields different results. Hence, we perform a sensitivity analysis for the main parameters of the inversion method, in order to understand how the subsurface model may be influenced by erroneous assumptions and parameter settings.
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: 206A (Anaheim Convention Center)
Presentation Type: Oral
This paper describes how geostatistical inversion based in a Bayesian framework can be modeled and applied on post-stack seismic data, yielding multiple stochastic realizations of acoustic impedance with improved vertical resolution and conditioned to well data. The proposed method is capable to jointly estimate not only the acoustic impedance, but also the wavelet and the uncertainties of the inversion results. The Gaussian assumption for the likelihood models enables to obtain the analytical expressions for the conditioned distributions, which allows sampling from the posterior distribution via Gibbs Algorithm. Here we propose a different convolutional model that simplifies the conditional distributions of the Gibbs algorithm, and discuss in detail how some variables of the stochastic model were defined in a geophysical interpretation. Results of tests on real data are compared with the deterministic Constrained Sparse Spike Inversion and, as expected, clearly show the improvement in the vertical resolution and the conditioning to well data.