Visualization of Massive Volumetric Data Sets Bradley Wallet
Visualization of Massive Volumetric Data Sets Bradley Wallet Robert Wentland Jawad Mokhtar brad@chroma-corp. com http: //www. chromaenergy. com/ 9 th February 1998 Shared Earth Technologies
Acknowledgements • Chroma for allowing me to come • Chroma Energy for allowing me to speak • PGS for allowing me to show their data 2
Outline • • Background (Goals and Data) Why Visualize? Hardware Software 3
Goals • Find economic hydrocarbon reservoirs – Reduce the number of dry holes – Locate leads that would otherwise be missed • Extract information necessary to exploit reservoirs 4
Data • • • Large 3 -D volumetric data set Pre-stack data (Amplitude vs. Offset) Reflection Coefficient data Acoustic Impedance data Derived (feature) data 4 -D Seismic data 5
Data • Data cubes consists of 100 million to 500+ million observations – Typically part of larger data set – Desire to work in 32 bits per voxel • Typically four or more derived data cubes – Some really should be 32 bits per voxel – Often need three or more in memory at a time 6
Data 7
Data 8
Data Gulf of Mexico 3 D Seismic 1 Sec Channel Sequence of Interest Done More Wait 9
Data 10
Data Access the pattern level of the Chroma. Cube. TM pattern database. Done More 11
Why Visualize • Technical feasibility – “Oil is found in the minds of men” • Conservative culture • Economics – – Shallow onshore well costs $100 k Deep onshore well costs $1 m On shelf well costs $10 m Deep water well costs $100 m 12
Hardware • Long term storage – Generally available in sufficient quantity – Slow but typically acceptable • Intersite transfer of data • Main memory – Limited to 2 GB in 32 bit systems – Limited to 8 GB in practice – Contiguous memory issues 13
Hardware • Graphics cards – Not designed for volumetric applications – View must be calculated in software • Volumetric cards – – Limited in the past to 256 MB Limited to greyscale New ones hold up to 8 GB Support RGBA • Exotic (transputer) solutions – Expensive hardware – Expensive software 14
Software • Custom software – Cpat • Batch operations • Calculates derived data – Chroma. Vision • Visualization • Interactive colormap adjustments • Extraction, annotation, editing, etc. • Commercial off the shelf (COTS) software – Data preprocessing – Standard operations 15
Software Workflow Data Acquisition Stacked Angle Stacks CMP (for AVO) Multi-component 4 D (time varying stacks) Data Processing Migrated Image Pattern Visualization Enabled Visualization Interpretation Well Plan Drilling Reservoir Simulation Seismic Data Current Workflow Pattern Analysis Pattern Database Pattern Enabled Workflow 16
Software • Color maps – – – RGBA HSVA Alpha channel Interactive exploration Grand tour 17
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