Geologic heterogeneity representation using high‐order spatial cumulants for subsurface flow and transport simulations

[1] The effects of geological heterogeneity representation on the hydraulic properties of two-dimensional flow and transport simulations are studied using various stochastic simulation algorithms. An alternative multiple-point method (HOSIM) on the basis of high-order spatial cumulants and Legendre polynomials is used and compared to the multiple-point FilterSIM method, and the sequential Gaussian simulation (SGS) method. Conditional realizations of a fluvial reservoir system are generated by HOSIM, FilterSIM, and SGS methods. Then, the simulated hydraulic permeability fields (K) are used in a numerical groundwater flow and solute transport models. Numerical results showed that the HOSIM method created greater connectivity in the reservoir/aquifer (channel) network than FilterSIM and SGS realizations. The numerical simulations show that in a reservoir/aquifer system with a strongly connected network of high-K materials, the Gaussian and FilterSIM approaches are not as effective as HOSIM in reproducing this behavior. The simulations showed a good agreement between HOSIM realizations and the exhaustive reference image.

[1]  F. Alabert,et al.  Non-Gaussian data expansion in the Earth Sciences , 1989 .

[2]  Timothy D. Scheibe,et al.  Simulation of Geologic Patterns: A Comparison of Stochastic Simulation Techniques for Groundwater Transport Modeling , 1998 .

[3]  R. Grayson,et al.  Toward capturing hydrologically significant connectivity in spatial patterns , 2001 .

[4]  A. Shiryaev Some Problems in the Spectral Theory of Higher-Order Moments. I , 1960 .

[5]  R. Dimitrakopoulos,et al.  Two-dimensional Conditional Simulations Based on the Wavelet Decomposition of Training Images , 2009 .

[6]  S. Gorelick,et al.  Heterogeneity in Sedimentary Deposits: A Review of Structure‐Imitating, Process‐Imitating, and Descriptive Approaches , 1996 .

[7]  Paul Switzer,et al.  Filter-Based Classification of Training Image Patterns for Spatial Simulation , 2006 .

[8]  J. Bear Dynamics of Fluids in Porous Media , 1975 .

[9]  Roussos Dimitrakopoulos,et al.  An efficient method for discretizing 3D fractured media for subsurface flow and transport simulations , 2011 .

[10]  J. Doyle,et al.  Which sub-seismic heterogeneities influence waterflood performance? A case study of a low net-to-gross fluvial reservoir , 1995, Geological Society, London, Special Publications.

[11]  L. Feyen,et al.  Reliable groundwater management in hydroecologically sensitive areas , 2004 .

[12]  A. Journel,et al.  Entropy and spatial disorder , 1993 .

[13]  Alexandre Boucher,et al.  A SGeMS code for pattern simulation of continuous and categorical variables: FILTERSIM , 2008, Comput. Geosci..

[14]  J. Gómez-Hernández,et al.  To be or not to be multi-Gaussian? A reflection on stochastic hydrogeology , 1998 .

[15]  Khalid M. Hosny,et al.  Exact Legendre moment computation for gray level images , 2007, Pattern Recognit..

[16]  A. Wragg,et al.  Numerical Methods for Approximating Continuous Probability Density Functions, over [0, ∞), Using Moments , 1973 .

[17]  Pablo Fosalba,et al.  Gravitational Evolution of the Large-Scale Probability Density Distribution: The Edgeworth and Gamma Expansions , 2000 .

[18]  M. Rosenblatt Stationary sequences and random fields , 1985 .

[19]  Miroslaw Pawlak,et al.  On Image Analysis by Moments , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Peter A. Forsyth,et al.  Grid refinement for modeling multiphase flow in discretely fractured porous media , 1999 .

[21]  Xian-Huan Wen,et al.  Numerical modeling of macrodispersion in heterogeneous media: a comparison of multi-Gaussian and non-multi-Gaussian models , 1998 .

[22]  Ruben Juanes,et al.  Post-injection spreading and trapping of CO2 in saline aquifers: impact of the plume shape at the end of injection , 2009 .

[23]  Jerry M. Mendel,et al.  Linear modeling of multidimensional non-gaussian processes using cumulants , 1990, Multidimens. Syst. Signal Process..

[24]  B. Matérn Spatial variation : Stochastic models and their application to some problems in forest surveys and other sampling investigations , 1960 .

[25]  N. Cressie,et al.  Statistics for Spatial Data. , 1992 .

[26]  A. Desbarats,et al.  Macrodispersion in sand‐shale sequences , 1990 .

[27]  J. Caers,et al.  Conditional Simulation with Patterns , 2007 .

[28]  R. M. Srivastava,et al.  Geostatistical characterization of groundwater flow parameters in a simulated aquifer , 1991 .

[29]  Amisha Maharaja,et al.  TiGenerator: Object-based training image generator , 2008, Comput. Geosci..

[30]  Alexandre Boucher,et al.  Considering complex training images with search tree partitioning , 2009, Comput. Geosci..

[31]  L. Hu,et al.  Multiple-Point Simulations Constrained by Continuous Auxiliary Data , 2008 .

[32]  Haakon Tjelmeland,et al.  Directional Metropolis : Hastings Updates for Posteriors with Nonlinear Likelihoods , 2005 .

[33]  Peter A. Forsyth,et al.  Numerical simulation of multiphase flow and phase partitioning in discretely fractured geologic media , 1999 .

[34]  S. Gorelick,et al.  Reliable aquifer remediation in the presence of spatially variable hydraulic conductivity: From data to design , 1989 .

[35]  M. David Handbook of Applied Advanced Geostatistical Ore Reserve Estimation , 1987 .

[36]  Håkon Tjelmeland,et al.  Markov Random Fields with Higher‐order Interactions , 1998 .

[37]  Charles F. Harvey,et al.  When good statistical models of aquifer heterogeneity go bad: A comparison of flow, dispersion, and mass transfer in connected and multivariate Gaussian hydraulic conductivity fields , 2003 .

[38]  Ruben Juanes,et al.  The Footprint of the CO2 Plume during Carbon Dioxide Storage in Saline Aquifers: Storage Efficiency for Capillary Trapping at the Basin Scale , 2010 .

[39]  C. P. North The prediction and modelling of subsurface fluvial stratigraphy , 1996 .

[40]  Jef Caers,et al.  Representing Spatial Uncertainty Using Distances and Kernels , 2009 .

[41]  Geoffrey S. Watson,et al.  Geostatistical Ore Reserve Estimation. , 1978 .

[42]  Steven F. Carle,et al.  Geologic heterogeneity and a comparison of two geostatistical models: Sequential Gaussian and transition probability-based geostatistical simulation , 2007 .

[43]  G. Fogg,et al.  Modeling Spatial Variability with One and Multidimensional Continuous-Lag Markov Chains , 1997 .

[44]  F. G. Alabert,et al.  Stochastic Models of Reservoir Heterogeneity: Impact on Connectivity and Average Permeabilities , 1992 .

[45]  R. Dimitrakopoulos,et al.  High-order Stochastic Simulation of Complex Spatially Distributed Natural Phenomena , 2010 .

[46]  Hassan Qjidaa,et al.  Skeletonization of Noisy Images via the Method of Legendre Moments , 2005, ACIVS.

[47]  Andre G. Journel,et al.  Spatial Connectivity: From Variograms to Multiple-Point Measures , 2003 .

[48]  Raveendran Paramesran,et al.  An efficient method for the computation of Legendre moments , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[49]  Sebastien Strebelle,et al.  Conditional Simulation of Complex Geological Structures Using Multiple-Point Statistics , 2002 .

[50]  Feng Zhang,et al.  A High Order Cumulants Based Multivariate Nonlinear Blind Source Separation Method , 2005, Machine Learning.

[51]  Xian-Huan Wen,et al.  Stochastic Simulation of Solute Transport in Heterogeneous Formations: A Comparison of Parametric and Nonparametric Geostatistical Approaches , 1993 .

[52]  M. Rosenblatt,et al.  ASYMPTOTIC THEORY OF ESTIMATES OF kTH-ORDER SPECTRA. , 1967, Proceedings of the National Academy of Sciences of the United States of America.

[53]  Graham E. Fogg,et al.  Groundwater Flow and Sand Body Interconnectedness in a Thick, Multiple-Aquifer System , 1986 .

[54]  Roussos Dimitrakopoulos,et al.  Discretizing two‐dimensional complex fractured fields for incompressible two‐phase flow , 2011 .

[55]  P. Forsyth,et al.  Importance of Rock Matrix Entry Pressure on DNAPL Migration in Fractured Geologic Materials , 1999 .

[56]  Steven F. Carle,et al.  Connected-network paradigm for the alluvial aquifer system , 2000 .

[57]  G. Marsily,et al.  Some current methods to represent the heterogeneity of natural media in hydrogeology , 1998 .

[58]  Jerry M. Mendel,et al.  Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications , 1991, Proc. IEEE.

[59]  Carl W. Gable,et al.  Equivalent hydraulic conductivity of an experimental stratigraphy: Implications for basin‐scale flow simulations , 2006 .

[60]  R. Allan Freeze,et al.  Stochastic analysis of steady state groundwater flow in a bounded domain: 2. Two‐dimensional simulations , 1979 .

[61]  R. Ababou,et al.  Numerical simulation of three-dimensional saturated flow in randomly heterogeneous porous media , 1989 .

[62]  Franklin W. Schwartz,et al.  mass transport: 2. Analysis of uncertainty in prediction , 1981 .

[63]  Chin-Fu Tsang,et al.  Flow channeling in strongly heterogeneous porous media: A numerical study , 1994 .

[64]  Roussos G. Dimitrakopoulos,et al.  A new approach for geological pattern recognition using high-order spatial cumulants , 2010, Comput. Geosci..

[65]  B. M. Rutherford,et al.  Stochastic Simultation for Imaging Spatial Uncertainty: Comparison and Evaluation of Available Algorithms , 1994 .

[66]  M. Anderson Subsurface Flow and Transport: Characterization of geological heterogeneity , 1997 .

[67]  S. P. Neuman Universal scaling of hydraulic conductivities and dispersivities in geologic media , 1990 .

[68]  Daniel M. Tetzlaff,et al.  Stationarity Scores on Training Images for Multipoint Geostatistics , 2009 .

[69]  Roussos Dimitrakopoulos,et al.  High-order Statistics of Spatial Random Fields: Exploring Spatial Cumulants for Modeling Complex Non-Gaussian and Non-linear Phenomena , 2009 .

[70]  Clayton V. Deutsch,et al.  GSLIB: Geostatistical Software Library and User's Guide , 1993 .

[71]  S. Gorelick,et al.  Combining geologic‐process models and geostatistics for conditional simulation of 3‐D subsurface heterogeneity , 2010 .

[72]  Timothy C. Coburn,et al.  Geostatistics for Natural Resources Evaluation , 2000, Technometrics.

[73]  Mary P. Anderson,et al.  Comment on “Universal scaling of hydraulic conductivities and dispersivities in geologic media” by S. P. Neuman , 1991 .

[74]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[75]  Roussos G. Dimitrakopoulos,et al.  HOSIM: A high-order stochastic simulation algorithm for generating three-dimensional complex geological patterns , 2011, Comput. Geosci..

[76]  C. L. Nikias,et al.  Higher-order spectra analysis : a nonlinear signal processing framework , 1993 .

[77]  Alberto Guadagnini,et al.  Convergence assessment of numerical Monte Carlo simulations in groundwater hydrology , 2004 .

[78]  R. M. Srivastava,et al.  Multivariate Geostatistics: Beyond Bivariate Moments , 1993 .

[79]  G. Fogg,et al.  Transition probability-based indicator geostatistics , 1996 .

[80]  Peter A. Forsyth,et al.  A Control Volume Finite Element Approach to NAPL Groundwater Contamination , 1991, SIAM J. Sci. Comput..

[81]  Colin Daly,et al.  Higher Order Models using Entropy, Markov Random Fields and Sequential Simulation , 2005 .