Probabilistic seismic inversion based on rock-physics models for reservoir characterization

A site-specific rock-physics transform from porosity, mineralogy, and pore fluid to elastic-wave velocities is used to invert seismic amplitude data for clay content, total porosity, and saturation. The implementation is Bayesian and produces probabilistic values of the reservoir properties from seismic measurements and well data. This method focuses on an exploration setting where minimal data exist. Two key assumptions reduce the problem and keep the prior information as noncommittal as possible. First, a prior interpretation of the seismic data is required that provides a geobody on which to perform the inversion. Second, the reservoir thickness is assumed to be constant, as are the rock properties within the reservoir. The prior distributions of the reservoir properties are assumed to be uncorrelated and independent, although this is not an essential assumption. Central to theinversion is the generation of a complete set of earth models derived from the prior distribution. A site-specific rock-physics...

[1]  D. B. Neff.  Incremental pay thickness modeling of hydrocarbon reservoirs , 1990 .

[2]  Miguel Bosch,et al.  Inversion of traveltime data under a statistical model for seismic velocities and layer interfaces , 2005 .

[3]  Luiz Geraldo Loures,et al.  Porosity inference and classification of siliciclastic rocks from multiple data sets , 2006 .

[4]  Amos Nur,et al.  Elasticity of high‐porosity sandstones: Theory for two North Sea data sets , 1996 .

[5]  A. Nur,et al.  Effects of porosity and clay content on wave velocities in sandstones , 1986 .

[6]  Alberto Malinverno,et al.  Two ways to quantify uncertainty in geophysical inverse problems , 2006 .

[7]  Raymond D. Mindlin,et al.  Compliance of elastic bodies in contact , 1949 .

[8]  Luis Tenorio,et al.  Prior information and uncertainty in inverse problems , 2001 .

[9]  Miguel Bosch,et al.  The optimization approach to lithological tomography: Combining seismic data and petrophysics for porosity prediction , 2004 .

[10]  A. Tarantola.  Inverse problem theory : methods for data fitting and model parameter estimation , 1987 .

[11]  A. Nur,et al.  Critical porosity; a key to relating physical properties to porosity in rocks , 1998 .

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

[13]  K. Spikes,et al.  Simultaneous Model-Based Inversion for Lithology, Porosity, and Fluid , 2005 .

[14]  L. Wasserman,et al.  The Selection of Prior Distributions by Formal Rules , 1996 .

[15]  L. L. Raymer,et al.  An Improved Sonic Transit Time-To-Porosity Transform , 1980 .

[16]  Bradley P. Carlin,et al.  BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS , 1996, Stat. Comput..

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

[18]  A. R. Gregory,et al.  ELASTIC WAVE VELOCITIES IN HETEROGENEOUS AND POROUS MEDIA , 1956 .

[19]  A. Nur,et al.  A physical model for porosity reduction in sandstones , 1998 .

[20]  K. Spikes,et al.  Gassmann-consistency of velocity-porosity transforms , 2005 .

[21]  S. Dosso,et al.  Uncertainty estimation for amplitude variation with offset (AVO) inversion , 2003 .

[22]  T. Louis,et al.  Bayes and Empirical Bayes Methods for Data Analysis. , 1997 .

[23]  R. Hill.  The Elastic Behaviour of a Crystalline Aggregate , 1952 .

[24]  S. Shtrikman,et al.  A variational approach to the theory of the elastic behaviour of multiphase materials , 1963 .

[25]  Y. Rubin,et al.  A Bayesian model for gas saturation estimation using marine seismic AVA and CSEM data , 2007 .

[26]  A. R. Gregory,et al.  An experimental investigation of factors affecting elastic wave velocities in porous media , 1958 .

[27]  T. Mukerji,et al.  The Rock Physics Handbook , 1998 .

[28]  Amos Nur,et al.  Elastic‐wave velocities in sandstones with non‐load‐bearing clay , 1999 .

[29]  E. C. Thomas,et al.  The Distribution Of Shale In Sandstones And Its Effect Upon Porosity , 1975 .

[30]  T. Mukerji,et al.  Fluid substitution: Estimating changes in VP without knowing VS , 1995 .

[31]  L. D. Boer,et al.  Using Bayesian simulations to predict reservoir thickness under tuning conditions , 1998 .

[32]  Herbert F. Wang,et al.  Velocity‐porosity‐clay content systematics of poorly consolidated sandstones , 1984 .

[33]  E. González.  Physical and quantitative interpretation of seismic attributes for rocks and fluids identification , 2006 .

[34]  A. Nur,et al.  Effects of diagenesis and clays on compressional velocities in rocks , 1982 .

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

[36]  D. B. Neff.  Amplitude map analysis using forward modeling in sandstone and carbonate reservoirs , 1993 .

[37]  H. Yin.  Acoustic velocity and attenuation of rocks : isotropy, intrinsic anisotropy, and stress-induced anisotropy , 1992 .

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

[39]  F. Gaßmann.  Uber die Elastizitat poroser Medien. , 1961 .

[40]  James Gunning,et al.  Delivery: an open-source model-based Bayesian seismic inversion program , 2004, Comput. Geosci..