Visualizing highly multidimensional time varying Microseismic Events

Making decisions about improving an oil and gas reservoir model based upon microseismic data is a difficult challenge for reservoir engineers and analysts. These difficulties arise because the available data contains inaccuracies, has high-dimensionality and has a high degree of uncertainty. Currently these difficulties are intensified by the lack of computational tools to support interactive visual interpretation and integration of geophysical data leading to robust structural models of the reservoir and its parameters. To address these difficulties domain experts are demanding better and more detailed visualization tools to help them as they explore their data. In this paper, we present a tool that contains a set of interactive visualizations that combines, merges and extends existing visualization techniques. We describe the iterative design process we undertook to develop the tool, relying on insight from domain specialists. Our tool supports 3D spatial analysis and exploration of the data with a set of manipulations designed to provide domain experts with insights into their highly complex microseismic data. Our microseismic visual-analysis tool also provides an extended parallel coordinates implementation to: (1) support interactive filtering and selection through combined filter and shadow boxes that can remove the uninteresting events from further analysis, (2) correlate between the data attributes by axes reordering and outlier discovery, and (3) visually correlate the data events rendering through additional visual elements such as color maps. Our multiple coordinated views link the insights gained from one view with other views instantaneously. We conclude with a discussion of the feedback provided to us by the domain experts.

[1]  Jason David Baihly,et al.  Real-Time Microseismic Monitoring of Hydraulic Fracture Treatment: A Tool To Improve Completion and Reservoir Management , 2007 .

[2]  W. Hays Semiology of Graphics: Diagrams Networks Maps. , 1985 .

[3]  Emmanuel Pietriga,et al.  Sigma lenses: focus-context transitions combining space, time and translucence , 2008, CHI.

[4]  Kwan-Liu Ma,et al.  Depicting Time Evolving Flow with Illustrative Visualization Techniques , 2009, ArtsIT.

[5]  Elaine Cohen,et al.  A non-photorealistic lighting model for automatic technical illustration , 1998, SIGGRAPH.

[6]  Silvia Miksch,et al.  Semantic depth of field , 2001, IEEE Symposium on Information Visualization, 2001. INFOVIS 2001..

[7]  Hong Zhou,et al.  Scattering Points in Parallel Coordinates , 2009, IEEE Transactions on Visualization and Computer Graphics.

[8]  Li Sha,et al.  An algorithm for locating microseismic events , 2004, Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No.04CH37513).

[9]  Matthew O. Ward,et al.  XmdvTool: integrating multiple methods for visualizing multivariate data , 1994, Proceedings Visualization '94.

[10]  T. J. Jankun-Kelly,et al.  Guided analysis of hurricane trends using statistical processes integrated with interactive parallel coordinates , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[11]  John T. Stasko,et al.  DataMeadow: A Visual Canvas for Analysis of Large-Scale Multivariate Data , 2007, IEEE VAST.

[12]  Shawn Maxwell,et al.  Microseismic: Growth born from success , 2010 .

[13]  Allison Woodruff,et al.  Guidelines for using multiple views in information visualization , 2000, AVI '00.

[14]  Stephen M. Omohundro,et al.  Five Balltree Construction Algorithms , 2009 .

[15]  Pierre Dragicevic,et al.  Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation , 2008, IEEE Transactions on Visualization and Computer Graphics.

[16]  Matthew O. Ward,et al.  Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering , 2004, IEEE Symposium on Information Visualization.

[17]  Harri Siirtola,et al.  Interacting with parallel coordinates , 2006, Interact. Comput..

[18]  N. R. Warpinski,et al.  Microseismic Monitoring: Inside and Out , 2009 .

[19]  David Feng,et al.  Linked exploratory visualizations for uncertain MR spectroscopy data , 2010, Electronic Imaging.

[20]  Alfred Inselberg,et al.  Parallel coordinates: a tool for visualizing multi-dimensional geometry , 1990, Proceedings of the First IEEE Conference on Visualization: Visualization `90.

[21]  Shantanu H. Joshi,et al.  Query-based coordinated multiple views with Feature Similarity Space for visual analysis of MRI repositories , 2011, 2011 IEEE Conference on Visual Analytics Science and Technology (VAST).

[22]  John Rugis,et al.  Towards Crustal Reservoir Flow Structure Modelling Through Interactive 3D Visualization of MEQ & MT Field Data , 2011 .

[23]  Paul Anderson,et al.  It's a matter of size: Magnitude and moment estimates for microseismic data , 2010 .

[24]  Tamara Munzner,et al.  Steerable, Progressive Multidimensional Scaling , 2004, IEEE Symposium on Information Visualization.

[25]  Matthew O. Ward,et al.  High Dimensional Brushing for Interactive Exploration of Multivariate Data , 1995, Proceedings Visualization '95.

[26]  N. Andrienko,et al.  Coordinated Multiple Views: a Critical View , 2007, Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007).

[27]  James Carlson,et al.  Multiattribute Visualization Using Multivariate Volume Rendering and Glyphs , 2011 .

[28]  J.C. Roberts,et al.  State of the Art: Coordinated & Multiple Views in Exploratory Visualization , 2007, Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization (CMV 2007).