GPU-based SNESIM implementation for multiple-point statistical simulation

Among techniques applied to categorical variables simulation, multiple-point statistical simulation is widely used because of its non-iterative characteristic and powerful capability of curvilinear features reproduction. In current implementations, the multiple-point statistics (MPS) are inferred from the training image by storing all the observed patterns scanned by data templates of a certain size within a data structure, either a tree used by Single Normal Equation Simulation Algorithm (SNESIM) or a list used by IMPALA. This type of algorithms has the advantage of being fast to be applied, but it presents some critical limitations. In particular, the data structure is extremely memory demanding. For large-scale problems with numerous patterns, large data templates cannot be used. Therefore, complex structures are then difficult to be simulated. A GPU computing scheme for SNESIM is proposed for multiple point statistical simulation in this paper. Taking advantage of powerful computing capability of GPU's many-core architecture, parallel operations are applied to each simulation grid node, which is the most time-consuming portion among entire simulation process. This scheme requires fixed size memory, so it is independent of inference of data template size, which is especially important for large-scale problems. The simulation results based on a NVIDIAGTX680 device can obtain about 15x speedup than on an Intel Core i3 540 CPU, which demonstrates the efficiency of the scheme against search-tree based implementation of SNESIM contained in SGeMS software.

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

[2]  M. Blunt,et al.  Pore space reconstruction using multiple-point statistics , 2005 .

[3]  Philippe Renard,et al.  3D multiple-point statistics simulation using 2D training images , 2012, Comput. Geosci..

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

[5]  Clayton V. Deutsch,et al.  ANNEALING TECHNIQUES APPLIED TO RESERVOIR MODELING AND THE INTEGRATION OF GEOLOGICAL AND ENGINEERING (WELL TEST) DATA , 1992 .

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

[7]  G. Mariéthoz,et al.  An Improved Parallel Multiple-point Algorithm Using a List Approach , 2011 .

[8]  Pejman Tahmasebi,et al.  Accelerating geostatistical simulations using graphics processing units (GPU) , 2012, Comput. Geosci..

[9]  Clayton V. Deutsch,et al.  Integrating Large-Scale Soft Data by Simulated Annealing and Probability Constraints , 2000 .

[10]  Grégoire Mariethoz,et al.  A general parallelization strategy for random path based geostatistical simulation methods , 2010, Comput. Geosci..

[11]  Hubert Nguyen,et al.  GPU Gems 3 , 2007 .

[12]  Thomas Rauber,et al.  Parallel Programming: for Multicore and Cluster Systems , 2010, Parallel Programming, 3rd Ed..

[13]  Jef Caers,et al.  Geostatistical Quantification of Geological Information for a Fluvial-Type North Sea Reservoir , 2000 .

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

[15]  Julián M. Ortiz,et al.  Multiple Point Geostatistical Simulation with Simulated Annealing: Implementation Using Speculative Parallel Computing , 2010 .

[16]  Yuhong Liu,et al.  Improving Sequential Simulation with a Structured Path Guided by Information Content , 2004 .

[17]  Gregoire Mariethoz,et al.  The Direct Sampling method to perform multiple‐point geostatistical simulations , 2010 .

[18]  3D Porosity Modeling of a Carbonate Reservoir Using Continuous Multiple-Point Statistics Simulation , 2006 .

[19]  Timothy G. Mattson,et al.  Patterns for parallel programming , 2004 .

[20]  A. Journel,et al.  Reservoir Modeling Using Multiple-Point Statistics , 2001 .

[21]  M. Blunt,et al.  Prediction of permeability for porous media reconstructed using multiple-point statistics. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[22]  Alexandre Boucher,et al.  Applied Geostatistics with SGeMS: A User's Guide , 2009 .

[23]  A. Journel,et al.  Hierarchical simulation of multiple-facies reservoirs using multiple-point geostatistics , 2005 .

[24]  Rúben Nunes,et al.  Parallelization of sequential Gaussian, indicator and direct simulation algorithms , 2010, Comput. Geosci..

[25]  Andre G. Journel,et al.  Geostatistics: Roadblocks and Challenges , 1993 .

[26]  L. Dagum,et al.  OpenMP: an industry standard API for shared-memory programming , 1998 .

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

[28]  Jef Caers,et al.  Sequential simulation with patterns , 2005 .

[29]  Yuhong Liu,et al.  An Information Content Measure Using Multiple-point Statistics , 2005 .

[30]  Julián M. Ortiz,et al.  Parallel implementation of simulated annealing to reproduce multiple-point statistics , 2011, Comput. Geosci..