Interactive Data Processing and 3D Visualization of the Solid Earth

Preface
Contents
Modeling Terrains and Subsurface Geology
1 Introduction
2 Geological Elements
3 Geomodelling Data Taxonomy
4 Geomodelling Workflow Taxonomy
4.1 Data-Free
4.2 Sparse and Dense Data
5 Comparing Surface and Solid Representations for Geomodels
5.1 Surfaces
5.2 Solids
6 Challenges and Trends in Geological Modelling
References
Real-Time Algorithms for Visualizing and Processing Seismic and Reservoir Data
1 Visualization of Seismic and Reservoir Data
1.1 Fundamentals of Seismic Volume Visualization and Interpretation
1.2 Visualizing Large Volumes
1.3 Visualizing Isosurfaces Using Pre-integration
1.4 Visualizing Horizons
1.5 Shadows and Shading
1.6 Reservoir Visualization
2 Advanced Visualization of Seismic and Reservoir Data
2.1 Illustrative Rendering
2.2 Exploded Views
2.3 Annotations and Visibility of Features
2.4 Advanced Reservoir Visualization
2.5 Privacy Preserving Volume Rendering
3 Processing of Seismic Data
3.1 Frequency Decomposition of Seismic Data
3.2 Computer-Aided Object Extraction
4 Summary
References
Overview of Seismic Attributes and Seismic Object Extraction
1 Introduction
1.1 Geologic Processes Forming the Subsurface
1.2 Seismic Reflection Data
1.3 Introduction to Seismic Attributes
1.4 Filtering of Seismic Data for Noise Removal and Feature Enhancement
1.5 Basic Seismic Attributes
1.6 Facies Terminations
2 Object Extraction from Seismic Volumes
2.1 Geologic Background
2.2 Extraction Algorithms for Horizon Surfaces
2.3 Extraction Algorithms for Fault Surfaces
2.4 Extraction Algorithms for Salt Bodies
3 Seismic Attributes and Machine Learning
4 Summary
References
Using Interactive Visualization and Machine Learning for Seismic Interpretation
1 Some Basics About Seismic Interpretation (SI)
2 User-Driven Seismic Volume Classification with Interactive Visualization
2.1 Multi-attribute Visualization
2.2 Seismic Volume Rendering and Transfer Functions
2.3 Histograms and Transfer Functions
3 Semi-automatic Detection of Anomalies in Seismic Data Based on Local Histogram Analysis
4 Detecting Faults and Channels with 3-D CNNs
4.1 Some Basics About Deep Learning
4.2 Related Work in Deep Learning
4.3 Deep Learning Experiments
4.4 Training Data Acquisition
4.5 The VRGeo Seismic Dataset
4.6 The Detecting Faults and Channels Results
5 Detecting Salt-Bodies with 3D CNNs
5.1 Seismic Interpretation Recap
5.2 Signal Processing Procedures
5.3 Methods
5.4 Dataset and Annotations
5.5 Data Augmentation
5.6 Training-, Validation- and Test-Set
5.7 Architecture, Voxel-Weighting, and Loss
5.8 Salt-Body Segmentation Results
6 Understanding CNNs for Seismic Feature Detection Better by Visualization
6.1 Visualization Methods
6.2 Activation Maximization
6.3 Guided Backpropagation
6.4 Grad-CAM
6.5 Guided Grad-CAM
6.6 Application to Seismic Data
6.7 Training Data
6.8 Classification Results
6.9 Visualization Results
7 DeepGeo—Streamlining AI Operations for Seismic Interpretation
7.1 Digital Transformation in the Oil and Gas Industry
7.2 Challenges in Adopting Machine Learning in an Organization
7.3 Ergonomic Aspects of ML Research and Development
7.4 Ergonomic Aspects of Collaboration
7.5 The DeepGeo Platform
7.6 System Architecture
7.7 The DeepGeo Proof-of-Concept
8 Conclusion, the Status Quo, and Next Steps
References
Multimodal Summed Area Tables—A Proof of Concept
1 Introduction
1.1 Visualizing Large Volumes
1.2 Subsampling and Correct Color Representation
1.3 Requirements for Accurate Volume Rendering
1.4 Related Work on Large Volume Visualization
2 Summed Area Tables for Approximating Histograms
2.1 Summed Area Tables Representation
2.2 How to Separate Data into Low and High Channel
2.3 Memory Consumption
2.4 Types of Data Supported
3 Results
3.1 MSAT for More Accurate Volume Rendering
3.2 MSAT for Interactive Classification
3.3 MSAT for Edge Preserving Smoothing
3.4 MSAT for Calculating Shadows in Volumes
3.5 MSAT for Frequency Filtering of Volumes
3.6 MSAT Adapted for Seismic Data
4 Conclusions and Future Work
References
Visualization of Large Scale Reservoir Models
1 Introduction
2 Related Work
3 Representation of Reservoir Grids
4 Rendering of Reservoir Grids
4.1 Rendering Reservoirs that Do not Fit into GPU Memory
4.2 Decimation of Cell Shapes
4.3 Decimation of Cell Values
5 Rendering Large Reservoirs Using GPU Steered Level-of-Detail
5.1 GPU Steered Level-of-Detail Selection
5.2 Visibility Counting
6 Results
6.1 Rendering Cell Outlines
6.2 Adding Shadows for Improved Depth Perception
7 Conclusions
References
When Visualization and Virtual Reality Made a Paradigm Shift in Oil and Gas
1 Introduction
2 Background
3 Early Focus and Development
3.1 Well Planning
3.2 Exploration Reconnaissance
4 Developing the VR Application
4.1 Production and Well Planning
4.2 Exploration Reconnaissance
4.3 Remote Collaboration
4.4 Importing Data into SHIVR
5 Integrating the VR Application with the Company’s Software Portfolio
5.1 Connecting to Another Running Application
5.2 Connecting to a Data Repository
5.3 Import Data from Other E&P Applications
6 Success Criteria and Lessons Learned
6.1 Main Benefits Achieved Through Use of VR and Large Screen Visualization
6.2 Risk Willing Management
6.3 Learning from Other Industries
6.4 Close Collaboration with Developers and User Involvement
6.5 Use of Pilot Operators
6.6 Integration of the VR Application with Existing Software Portfolio
6.7 The Difficult Balance Between Development of New Functionality and Bug-Fixing
6.8 Remote Collaboration—Great Potential but Low Usage
6.9 Adapting to Major Hardware Developments During the Project Period
6.10 Increasing Request for a Desktop Version of the VR Software
6.11 Commercialization
6.12 The Value of VR Technology for Branding
7 From CAVE to Head Mounted Displays
7.1 Experiences Using SHIVR in HMD
8 Conclusion
Appendix
References
Evolution of VR Software and Hardware for Explosion and Fire Safety Assessment and Training
1 Introduction
2 Background
2.1 Evolution of VR Hardware and Software
2.2 Experiences with VRSafety
3 Head Mounted Displays and Game Engines
3.1 Evolution of HMDs
3.2 The Oculus Rift HMD
3.3 Unity and Unreal Engine 3D Software
4 Porting from VRSafety to a Modern Head Mounted Display and Game Engine
4.1 Porting of Geometry Models
4.2 Implementing Software Functionality
5 Modes of Work in VR
6 The Immersive Experience in VRFlacs Compared to VRSafety
7 Discussion and Conclusions
References
Groupware for Research on Subsurface CO2 Storage
1 Introduction
1.1 Related Work
1.2 Systematic Approach
2 Achieving Overview (Activity 1)
3 Iteration 1: 3D Commercial Software as Groupware
3.1 Collecting and Structuring CCS Datasets (Activity 2)
3.2 Groupware Functionality Requirements (Activity 3)
3.3 Selection of Software to Evaluate
3.4 Description of 3D Platform
3.5 Evaluation of 3D Platform
3.6 Response on the 3D Platform (Activity 4)
3.7 Updated Version of Tested Software
4 Iteration 2: 2D in Web Browser as Groupware
4.1 Collection of More Data (Activity 2)
4.2 Description of 2D Web Platform (Activity 3)
4.3 Evaluation of 2D Platform
4.4 Response on the 2D Platform (Activity 4)
4.5 Comparison of 2D Web and 3D Standalone Platform
5 Iteration 3: 3D in Web Browser as Groupware
5.1 Results if the 2D Web and 3D Web Solutions Were Combined
6 Results and Findings
6.1 Results
6.2 Findings
7 Discussion and Summary
7.1 Discussion
7.2 Summary
References
Subsurface Evaluation Through Multi-scenario Reasoning
1 Introduction
2 Subsurface Evaluation in the Oil and Gas Exploration
3 Conceptualized Geological History
3.1 Example 1—Petroleum System
3.2 Example 2—Erosion
4 Logical Frameworks
4.1 Description Logics
4.2 Rewriting Logic
5 Modeling Geology in Space
5.1 Spatial Formalization and Reasoning
5.2 Modeling the Geological Spatial Relations
6 Modeling Geological Processes and Time
6.1 The Structure of System Configurations
6.2 Time Advance
6.3 Geological Processes
6.4 Modeling Petroleum System
7 GeMS: A Geological Multi-scenario Reasoning Framework
7.1 The GeMS Methodology
7.2 A Scalable and Flexible Architecture
7.3 Geological Scenarios
8 Technology Directions
References