Seismic attribute selection for unsupervised seismic facies analysis using user guided data-adaptive weights

With the rapid development in seismic attribute and interpretation techniques, interpreters can be overwhelmed by the number of attributes at their disposal. Pattern recognition driven seismic facies analysis provides a means to identify subtle variations across multiple attributes that may only be partially defined on a single attribute. Typically, interpreters intuitively choose input attributes for multiattribute facies analysis based on their experience and geologic target of interest. However, such an approach may overlook unsuspected features hidden in the data. We therefore augment this qualitative attribute selection process with quantitative measures of which candidate attributes best differentiate features of interest. Instead of selecting a group of attributes and assuming all the selected attributes contribute equally to the facies map, we weight the interpreter-selected input attributes based on both their response from the unsupervised learning algorithm and interpreter’s knowledge. In other...

[1]  UWE STRECKER,et al.  Data mining of 3 D poststack seismic attribute volumes using Kohonen self-organizing maps , 2002 .

[2]  Thierry Coléou,et al.  Interpreter's Corner—Unsupervised seismic facies classification: A review and comparison of techniques and implementation , 2003 .

[3]  James S. Schuelke,et al.  Validation: A Technique For Selecting Seismic Attributes And Verifying Results , 1998 .

[4]  Yanghua Wang,et al.  Porosity prediction using the group method of data handling , 2011 .

[5]  Arthur E. Barnes.  Redundant And Useless Seismic Attributes , 2006 .

[6]  P. Mahalanobis.  On the generalized distance in statistics , 1936 .

[7]  Chao Shao,et al.  Distance-Preserving SOM: A New Data Visualization Algorithm , 2012, J. Softw..

[8]  Thomas Smith,et al.  Geologic pattern recognition from seismic attributes: Principal component analysis and self-organizing maps , 2015 .

[9]  Ghassan AlRegib,et al.  Automated salt-dome detection using an attribute ranking framework with a dictionary-based classifier , 2017 .

[10]  Kevin P. Dorrington,et al.  Genetic‐algorithm/neural‐network approach to seismic attribute selection for well‐log prediction , 2004 .

[11]  M. Matos,et al.  Unsupervised seismic facies analysis using wavelet transform and self-organizing maps , 2007 .

[12]  Dengliang Gao,et al.  Volume texture extraction for 3D seismic visualization and interpretation , 2003 .

[13]  S. Eiho,et al.  Processing of RI-Angiocardiographic Images , 1976 .

[14]  K. Marfurt,et al.  Seismic Color Self-Organizing Maps , 2009 .

[15]  B. Hart,et al.  Approaches to defining reservoir physical properties from 3-D seismic attributes with limited well control: An example from the Jurassic Smackover Formation, Alabama , 2000 .

[16]  Kurt J. Marfurt,et al.  A window into the Proterozoic: Integrating 3D seismic, gravity, and magnetic data to image subbasement structures in the southeast Fort Worth basin , 2013 .

[17]  Richard Uden,et al.  Data mining of 3D poststack seismic attribute volumes using Kohonen self-organizing maps , 2002 .

[18]  Quincy Chen,et al.  Seismic attribute technology for reservoir forecasting and monitoring , 1997 .

[19]  Daniel M. Jarvie,et al.  Geologic framework of the Mississippian Barnett Shale, Barnett-Paleozoic total petroleum system, Bend arch–Fort Worth Basin, Texas , 2007 .

[20]  Guangmin Hu,et al.  Multi-waveform classification for seismic facies analysis , 2017, Comput. Geosci..

[21]  Jeffrey Marc Yarus,et al.  Stochastic Modeling and Geostatistics , 1994 .

[22]  L. Land,et al.  Late-Stage Dolomitization of the Lower Ordovician Ellenburger Group, West Texas , 1991 .

[23]  C. Kerans.  Karst-Controlled Reservoir Heterogeneity in Ellenburger Group Carbonates of West Texas , 1988 .

[24]  Arthur E. Barnes,et al.  Investigation of Methods For Unsupervised Classification of Seismic Data , 2002 .

[25]  R. E. Abdel-Aal,et al.  Reservoir property prediction using abductive networks , 2010 .

[26]  Bruce S. Hart,et al.  Comparison of linear regression and a probabilistic neural network to predict porosity from 3‐D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico , 2001 .

[27]  A. M. Taratorin,et al.  Digital Processing of Biomedical Dynamical Images , 1988 .

[28]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[29]  John Quirein,et al.  Use of multiattribute transforms to predict log properties from seismic data , 2001 .

[30]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[31]  Fangyu Li,et al.  Constraining self-organizing map facies analysis with stratigraphy: An approach to increase the credibility in automatic seismic facies classification , 2017 .

[32]  Bo Zhang,et al.  Attribute expression of fault-controlled karst — Fort Worth Basin, Texas: A tutorial , 2014 .

[33]  Bruce S. Hart,et al.  A visual data-mining methodology for seismic facies analysis: Part 1 — Testing and comparison with other unsupervised clustering methods , 2009 .

[34]  E. C. Sullivan,et al.  Application of new seismic attributes to collapse chimneys in the Fort Worth Basin , 2006 .

[35]  A. J. Desbarats.  Stochastic modeling and geostatistics: Principles, methods and case studies AAPG computer applications in geology No 3 , 1996 .

[36]  Jing Zhang,et al.  Characterizing a turbidite system in Canterbury Basin, New Zealand, using seismic attributes and distance-preserving self-organizing maps , 2016 .

[37]  Jinghuai Gao,et al.  Joint rough sets and Karhunen-Loève transform approach to seismic attribute selection for porosity prediction in a Chinese sandstone reservoir , 2015 .

[38]  Kurt J. Marfurt,et al.  Integrated seismic texture segmentation and cluster analysis applied to channel delineation and chert reservoir characterization , 2011 .

[39]  Vikram Jayaram,et al.  A comparison of classification techniques for seismic facies recognition , 2015 .

[40]  Arthur E. Barnes,et al.  Redundant and useless seismic attributes , 2007 .

[41]  Haibin Di,et al.  Non-linear GLCM texture analysis for improved seismic facies interpretation , 2017 .

[42]  K. Benabdeslem,et al.  Feature Selection for Self-Organizing Map , 2007, 2007 29th International Conference on Information Technology Interfaces.

[43]  Kostia Azbel,et al.  A new methodology based on seismic facies analysis and litho-seismic modeling The Elkhorn Slough field pilot project, Solano County, California. , 1999 .

[44]  Atish Roy.  Latent space classification of seismic facies , 2013 .

[45]  K. Marfurt,et al.  Identification of brittle/ductile areas in unconventional reservoirs using seismic and microseismic data: Application to the Barnett Shale , 2015 .

[46]  C. Kalkomey,et al.  Potential risks when using seismic attributes as predictors of reservoir properties , 1997 .

[47]  Kurt J. Marfurt,et al.  Generative topographic mapping for seismic facies estimation of a carbonate wash, Veracruz Basin, southern Mexico , 2014 .