Probabilistic petrophysical-properties estimation integrating statistical rock physics with seismic inversion

A joint estimation of petrophysical properties is proposed that combines statistical rock physics and Bayesian seismic inversion. Because elastic attributes are correlated with petrophysical variables (effective porosity, clay content, and water saturation) and this physical link is associated with uncertainties, the petrophysical-properties estimation from seismic data can be seen as a Bayesian inversion problem. The purpose of this work was to develop a strategy for estimating the probability distributions of petrophysical parameters and litho-fluid classes from seismics. Estimation of reservoir properties and the associated uncertainty was performed in three steps: linearized seismic inversion to estimate the probabilities of elastic parameters, probabilistic upscaling to include the scale-changes effect, and petrophysical inversion to estimate the probabilities of petrophysical variables andlitho-fluid classes. Rock-physics equations provide the linkbetween reservoir properties and velocities, and lin...

[1]  G. Backus.  Long-Wave Elastic Anisotropy Produced by Horizontal Layering , 1962 .

[2]  Arild Buland,et al.  Bayesian lithology/fluid prediction and simulation on the basis of a Markov-chain prior model , 2006 .

[3]  E. Della Rossa,et al.  Ensemble Kalman Filter for Gaussian Mixture Models , 2007 .

[4]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[5]  Tapan Mukerji,et al.  Seismic inversion combining rock physics and multiple-point geostatistics , 2008 .

[6]  Tapan Mukerji,et al.  Stochastic reservoir characterization using prestack seismic data , 2004 .

[7]  K. Spikes,et al.  Probabilistic seismic inversion based on rock-physics models for reservoir characterization , 2007 .

[8]  Athanasios Papoulis,et al.  Probability, Random Variables and Stochastic Processes , 1965 .

[9]  C. Burns.  Seismic Reservoir Characterization , 1999 .

[10]  Tapan Mukerji,et al.  Quantitative Seismic Interpretation: References , 2005 .

[11]  Ran Bachrach,et al.  Joint estimation of porosity and saturation using stochastic rock-physics modeling , 2006 .

[12]  A. Morelli.  Inverse Problem Theory , 2010 .

[13]  Jeremy Gallop.  Facies probability from mixture distributions with non‐stationary impedance errors , 2006 .

[14]  Tapan Mukerji,et al.  Mapping lithofacies and pore‐fluid probabilities in a North Sea reservoir: Seismic inversions and statistical rock physics , 2001 .

[15]  Carla Carvajal,et al.  Petrophysical seismic inversion conditioned to well-log data: Methods and application to a gas reservoir , 2009 .

[16]  T. Mukerji,et al.  The Rock Physics Handbook: Contents , 2009 .

[17]  Arild Buland,et al.  Bayesian lithology and fluid prediction from seismic prestack data , 2008 .

[18]  M. Glinsky,et al.  Detection of reservoir quality using Bayesian seismic inversion , 2007 .

[19]  Statistical scale-up of reservoir properties: concepts and applications , 2004 .

[20]  R. Bachrach,et al.  Uncertainty in seismic-based pay volume estimation: Analysis using rock physics and Bayesian statistics , 2007 .

[21]  A. Buland,et al.  Bayesian linearized AVO inversion , 2003 .

[22]  J. Dvorkin,et al.  Fluid substitution in shaley sediment using effective porosity , 2007 .