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Meta–attributes and Artificial Networking: A New T ool for Seismic Interpretation de S Sain

Meta–attributes and Artificial Networking: A New T ool for Seismic Interpretation: Special Publications

Autor S Sain
en Limba Engleză Hardback – 7 iul 2022
Applying machine learning to the interpretation of seismic data Seismic data gathered on the surface can be used to generate numerous seismic attributes that enable better understanding of subsurface geological structures and stratigraphic features. With an ever-increasing volume of seismic data available, machine learning augments faster data processing and interpretation of complex subsurface geology. Meta-Attributes and Artificial Networking: A New Tool for Seismic Interpretation explores how artificial neural networks can be used for the automatic interpretation of 2D and 3D seismic data. Volume highlights include: * Historic evolution of seismic attributes * Overview of meta-attributes and how to design them * Workflows for the computation of meta-attributes from seismic data * Case studies demonstrating the application of meta-attributes * Sets of exercises with solutions provided * Sample data sets available for hands-on exercises The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
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Specificații

ISBN-13: 9781119482000
ISBN-10: 1119482003
Pagini: 288
Dimensiuni: 150 x 242 x 19 mm
Greutate: 0.54 kg
Editura: Wiley
Seria Special Publications

Locul publicării:Hoboken, United States

Notă biografică

Kalachand Sain, Wadia Institute of Himalayan Geology, India Priyadarshi Chinmoy Kumar, Wadia Institute of Himalayan Geology, India

Cuprins

Preface About the Authors Abbreviations List of Symbols and Operators PART I: SEISMIC ATTRIBUTES 1. An Overview of Seismic Attributes 1.1 Introduction 1.2 Historical evolution of seismic attributes 1.3 Characteristics of Seismic Attributes 1.4 A glance at seismic characteristics 1.4.1 Amplitude 1.4.2 Phase 1.4.3 Frequency 1.4.4 Bandwidth 1.4.5 Amplitude Change 1.4.6 Slope Dip and Azimuth 1.4.7 Curvature 1.4.8 Seismic Discontinuity 1.5 Summary References 2. Complex Trace, Structural and Stratigraphic Attributes 2.1 Introduction 2.2 Complex Trace Attributes: Mathematical Formulations and Derivations 2.3 Other Derived Complex Trace Attributes 2.3.1 Instantaneous Frequency 2.3.2 Sweetness 2.3.3 Relative Amplitude Change and Instantaneous Bandwidth 2.3.4 RMS Frequency 2.3.5 Q-factor 2.4 Structural and Stratigraphic Attributes 2.4.1 Dip and Azimuth Attributes Slope and Dip Exaggeration Dip-steering 2.4.2 Coherence Attribute 2.4.3 Similarity Attribute 2.4.4 Curvature Attribute 2.4.5 Advanced structural attributes Ridge Enhancement Filter (REF) attribute Thin Fault Likelihood (TFL) attribute Pseudo Relief attribute 2.4.6 Amplitude Variance 2.4.7 Reflection Spacing 2.4.8 Reflection Divergence 2.4.9 Reflection Parallelism 2.4.10 Spectral Decomposition 2.4.11 Velocity, Reflectivity and Attenuation attributes 2.5 A glance on interpretation pitfalls 2.6 Summary References 3. Be an Interpreter: Brainstorming Session 3.1 Task 1 3.2 Task 2 3.3 Task 3 3.4 Task 4 3.5 Task 5 3.6 Task 6 3.7 Task 7 3.8 Task 8 3.9 Task 9 3.10 Task 10 PART II: META-ATTRIBUTES 4. An Overview of Meta-attributes 4.1 Introduction 4.2 Meta-attributes 4.3 Types of Meta-attributes 4.3.1 Hydrocarbon Probability meta-attribute 4.3.2 Chimney Cube meta-attribute 4.3.3 Fault Cube meta-attribute 4.3.4 Intrusion Cube meta-attribute 4.3.5 Sill Cube meta-attribute 4.3.6 Mass Transport Deposit Cube meta-attribute 4.3.7 Lithology meta-attribute 4.4 Summary References 5. An Overview of Artificial Neural Networks 5.1 Introduction 5.2 Historical Evolution 5.3 Biological Neuron Vs Mathematical Neuron 5.3.1 Biological Neuron 5.3.2 Mathematical Neuron 5.4 Activation or Transfer Function 5.5 Types of Learning 5.6 Multi-layer Perceptron (MLP) and the Backpropagation Algorithm 5.7 Different Types of ANNs 5.7.1 Radial Basis Function (RBF) Network 5.7.2 Probabilistic Neural Network (PNN) 5.7.3 Generalized Regression Neural Network (GRNN) 5.7.4 Modular Neural Network (MNN) 5.7.5 Self Organizing Maps (SOM) 5.8 Summary References 6. How to Design Meta-attributes 6.1 Introduction 6.2 Meta-attribute design 6.2.1 Seismic Data conditioning Mean Filter (or Running-Average filter) Median Filter Alpha-Trimmed Mean Filter 6.2.2 Selection and Extraction of Seismic Attributes 6.2.3 Example Location 6.2.4 NN operation Evaluation of intelligent neural model 6.2.5 Validation 6.3 RGB Blending and Geo-body Extraction 6.4 Summary References PART III: CASE STUDIES OF META-ATTRIBUTES 7. Chimney interpretation using meta-attribute 7.1 Gas Chimneys: a clue for hydrocarbon exploration 7.2 Research Methodology 7.3 Chimney Validation 7.3.1 Geological Validation 7.3.2 Petrophysical Validation 7.3.3 Soft sediment deformation anomalies 7.4 Interpretation using Chimney Cube 7.5 Summary References 8. Fault Interpretation Using Meta-attribute 8.1 Fault meta-attribute: a motivation 8.2 Research Methodology 8.3 Results and Interpretation 8.4 Efficiency of the optimized TFC 8.5 Summary References 9. Fault and Fluid Migration Interpretation Using Meta-attribute 9.1 Introduction 9.2 Geophysical Data 9.3 Results and Interpretation 9.3.1 Thinned Fault Cube (TFC) and Fluid Cube (FlC) 9.3.2 Neural Design for the TFC and FlC 9.3.3 Interpretation using TFC and FlC 9.4 Summary References 10. Magmatic Sill Interpretation Using Meta-attribute (Part 1: Taranaki Basin example) 10.1 Magmatic Sills: Interpretation techniques 10.2 Research Methods 10.2.1 Structural conditioning 10.2.2 Selection of attributes 10.2.3 Example Locations 10.2.4 Neural Network 10.2.5 Validation 10.3 Results and Interpretation 10.4 Discussion 10.4.1 Sill cube an efficient interpretation tool for magmatic sills 10.4.2 Limitations of the Sill Cube automated approach 10.5 Conclusions References 11. Magmatic Sill Interpretation Using Meta-attribute (Part 2: Vøring Basin example) 11.1 Introduction: The Vøring Basin case 11.2 Description of the Data 11.3 Interpretation based on SC meta-attribute computation 11.4 Summary References 12. Magmatic Sill and Fluid Plumbing Interpretation Using Meta-attribute (Canterbury Basin example) 12.1 Introduction: The Canterbury Basin case 12.2 Description of the Data 12.3 Results and Interpretation 12.3.1 Data Enhancement, Attribute Analysis and Neural Operation 12.3.2 Interpretation through Sill Cube (SC) and Fluid Cube (FlC) meta-attributes 12.3.3 Limitation of the automated approach 12.4 Summary References 13. Volcanic System Interpretation Using Meta-attribute 13.1 Introduction 13.2 Research Workflow 13.3 Results and Interpretation 13.3.1 Seismic Data Enhancement 13.3.2 Neural Networks: Analysis and Optimization 13.3.3 Geologic interpretation using IC meta-attribute 13.3.4 Validation of the IC meta-attribute 13.4 Summary References 14. Interpretation of Mass Transport Deposits Using Meta-attribute 14.1 Introduction 14.2 Data and Research Workflow 14.3 Results and Interpretation 14.4 Summary References Appendix A A.1 Mathematical formulation of some common series and transformation A.1.1 Fourier Series A.1.2 Fourier and Inverse Fourier Transforms A.1.3 Hilbert Transform A.1.4 Convolution A.2 Dip-Steering Appendix B B.1 Answers to seismic cross-section interpretation (Tasks 1-6) B.2 Answers to numerical tasks (Tasks 7-10) Glossary