GEOMODELLING OF A FLUVIAL SYSTEM WITH SEMI-SUPERVISED SUPPORT VECTOR REGRESSION

Fluvial deposits are a challenge for modelling flow in sub-surface reservoirs. C onnectivity and continuity of permeable bodies have a major impact on fluid flow in porous media. Contemporary object-based and multipoint statistics methods face a problem of robust representation of connected structures. An alternative approach to model petrophysical properties is based on machine learning algorithm – Support Vector Regression (SVR). Semi-supervised SVR is able to establish spatial connectivity taking into account the prior knowledge on natural similarities. SVR as a learning algorithm is robust to noise and captures dependencies from all available data. Semi-supervised SVR applied to a synthetic fluvial reservoir demonstrated robust results, which are well matched to the flow performance.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

[3]  Samy Bengio,et al.  Semi-Supervised Kernel Methods for Regression Estimation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[4]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[5]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001, Statistical Science.

[6]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[7]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[8]  Mikhail Kanevski.  Advanced Mapping of Environmental Data , 2008 .

[9]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[10]  Michael Andrew Christie,et al.  Prediction under uncertainty in reservoir modeling , 2002 .

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

[12]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[13]  Mikhail Kanevski.  Advanced Mapping of Environmental Data/Geostatistics, Machine Learning and Bayesian Maximum Entropy , 2008 .

[14]  Mikhail Belkin,et al.  Beyond the point cloud: from transductive to semi-supervised learning , 2005, ICML.