Meta-Attributes and Artificial Networking: A New Tool for Seismic Interpretation

ISBN-13: 9781119482000 / Angielski / Twarda / 2022 / 304 str.

Kalachand Sain;Priyadarshi Chinmoy Kumar

Spis treści

PrefaceAbout the AuthorsAbbreviationsList of Symbols and OperatorsPART I: SEISMIC ATTRIBUTES1. An Overview of Seismic Attributes1.1 Introduction1.2 Historical evolution of seismic attributes1.3 Characteristics of Seismic Attributes1.4 A glance at seismic characteristics1.4.1 Amplitude1.4.2 Phase1.4.3 Frequency1.4.4 Bandwidth1.4.5 Amplitude Change1.4.6 Slope Dip and Azimuth1.4.7 Curvature1.4.8 Seismic Discontinuity1.5 SummaryReferences2. Complex Trace, Structural and Stratigraphic Attributes2.1 Introduction2.2 Complex Trace Attributes: Mathematical Formulations and Derivations2.3 Other Derived Complex Trace Attributes2.3.1 Instantaneous Frequency2.3.2 Sweetness2.3.3 Relative Amplitude Change and Instantaneous Bandwidth2.3.4 RMS Frequency2.3.5 Q-factor2.4 Structural and Stratigraphic Attributes2.4.1 Dip and Azimuth AttributesSlope and Dip ExaggerationDip-steering2.4.2 Coherence Attribute2.4.3 Similarity Attribute2.4.4 Curvature Attribute2.4.5 Advanced structural attributesRidge Enhancement Filter (REF) attributeThin Fault Likelihood (TFL) attributePseudo Relief attribute2.4.6 Amplitude Variance2.4.7 Reflection Spacing2.4.8 Reflection Divergence2.4.9 Reflection Parallelism2.4.10 Spectral Decomposition2.4.11 Velocity, Reflectivity and Attenuation attributes2.5 A glance on interpretation pitfalls2.6 SummaryReferences3. Be an Interpreter: Brainstorming Session3.1 Task 13.2 Task 23.3 Task 33.4 Task 43.5 Task 53.6 Task 63.7 Task 73.8 Task 83.9 Task 93.10 Task 10PART II: META-ATTRIBUTES4. An Overview of Meta-attributes4.1 Introduction4.2 Meta-attributes4.3 Types of Meta-attributes4.3.1 Hydrocarbon Probability meta-attribute4.3.2 Chimney Cube meta-attribute4.3.3 Fault Cube meta-attribute4.3.4 Intrusion Cube meta-attribute4.3.5 Sill Cube meta-attribute4.3.6 Mass Transport Deposit Cube meta-attribute4.3.7 Lithology meta-attribute4.4 SummaryReferences5. An Overview of Artificial Neural Networks5.1 Introduction5.2 Historical Evolution5.3 Biological Neuron Vs Mathematical Neuron5.3.1 Biological Neuron5.3.2 Mathematical Neuron5.4 Activation or Transfer Function5.5 Types of Learning5.6 Multi-layer Perceptron (MLP) and the Backpropagation Algorithm5.7 Different Types of ANNs5.7.1 Radial Basis Function (RBF) Network5.7.2 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 SummaryReferences6. How to Design Meta-attributes6.1 Introduction6.2 Meta-attribute design6.2.1 Seismic Data conditioningMean Filter (or Running-Average filter)Median FilterAlpha-Trimmed Mean Filter6.2.2 Selection and Extraction of Seismic Attributes6.2.3 Example Location6.2.4 NN operationEvaluation of intelligent neural model6.2.5 Validation6.3 RGB Blending and Geo-body Extraction6.4 SummaryReferencesPART III: CASE STUDIES OF META-ATTRIBUTES7. Chimney interpretation using meta-attribute7.1 Gas Chimneys: a clue for hydrocarbon exploration7.2 Research Methodology7.3 Chimney Validation7.3.1 Geological Validation7.3.2 Petrophysical Validation7.3.3 Soft sediment deformation anomalies7.4 Interpretation using Chimney Cube7.5 SummaryReferences8. Fault Interpretation Using Meta-attribute8.1 Fault meta-attribute: a motivation8.2 Research Methodology8.3 Results and Interpretation8.4 Efficiency of the optimized TFC8.5 SummaryReferences9. Fault and Fluid Migration Interpretation Using Meta-attribute9.1 Introduction9.2 Geophysical Data9.3 Results and Interpretation9.3.1 Thinned Fault Cube (TFC) and Fluid Cube (FlC)9.3.2 Neural Design for the TFC and FlC9.3.3 Interpretation using TFC and FlC9.4 SummaryReferences10. Magmatic Sill Interpretation Using Meta-attribute (Part 1: Taranaki Basin example)10.1 Magmatic Sills: Interpretation techniques10.2 Research Methods10.2.1 Structural conditioning10.2.2 Selection of attributes10.2.3 Example Locations10.2.4 Neural Network10.2.5 Validation10.3 Results and Interpretation10.4 Discussion10.4.1 Sill cube an efficient interpretation tool for magmatic sills10.4.2 Limitations of the Sill Cube automated approach10.5 ConclusionsReferences11. Magmatic Sill Interpretation Using Meta-attribute (Part 2: Vøring Basin example)11.1 Introduction: The Vøring Basin case11.2 Description of the Data11.3 Interpretation based on SC meta-attribute computation11.4 SummaryReferences12. Magmatic Sill and Fluid Plumbing Interpretation Using Meta-attribute (Canterbury Basin example)12.1 Introduction: The Canterbury Basin case12.2 Description of the Data12.3 Results and Interpretation12.3.1 Data Enhancement, Attribute Analysis and Neural Operation12.3.2 Interpretation through Sill Cube (SC) and Fluid Cube (FlC) meta-attributes12.3.3 Limitation of the automated approach12.4 SummaryReferences13. Volcanic System Interpretation Using Meta-attribute13.1 Introduction13.2 Research Workflow13.3 Results and Interpretation13.3.1 Seismic Data Enhancement13.3.2 Neural Networks: Analysis and Optimization13.3.3 Geologic interpretation using IC meta-attribute13.3.4 Validation of the IC meta-attribute13.4 SummaryReferences14. Interpretation of Mass Transport Deposits Using Meta-attribute14.1 Introduction14.2 Data and Research Workflow14.3 Results and Interpretation14.4 SummaryReferencesAppendix AA.1 Mathematical formulation of some common series and transformationA.1.1 Fourier SeriesA.1.2 Fourier and Inverse Fourier TransformsA.1.3 Hilbert TransformA.1.4 ConvolutionA.2 Dip-SteeringAppendix BB.1 Answers to seismic cross-section interpretation (Tasks 1-6)B.2 Answers to numerical tasks (Tasks 7-10)Glossary

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