Seismic Inversion Methods: A Practical Approach (Springer Geophysics)

✍ Scribed by S. P. Maurya, N. P. Singh, K. H. Singh

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 221
• Series: Springer Geophysics
• Edition: 1
• Category: Library

✦ Synopsis

This book introduces readers to seismic inversion methods and their application to both synthetic and real seismic data sets. Seismic inversion methods are routinely used to estimate attributes like P-impedance, S-impedance, density, the ratio of P-wave and S-wave velocities and elastic impedances from seismic and well log data. These attributes help to understand lithology and fluid contents in the subsurface. There are several seismic inversion methods available, but their application and results differ considerably, which can lead to confusion. This book explains all popular inversion methods, discusses their mathematical backgrounds, and demonstrates their capacity to extract information from seismic reflection data. The types covered include model-based inversion, colored inversion, sparse spike inversion, band-limited inversion, simultaneous inversion, elastic impedance inversion and geostatistical inversion, which includes single-attribute analysis, multi-attribute analysis, probabilistic neural networks and multi-layer feed-forward neural networks. In addition, the book describes local and global optimization methods and their application to seismic reflection data. Given its multidisciplinary, integrated and practical approach, the book offers a valuable tool for students and young professionals, especially those affiliated with oil companies.

✦ Table of Contents

Contents
1 Fundamental of Seismic Inversion
1.1 Introduction
1.2 Seismic Forward Modeling
1.3 Seismic Inversion
1.4 The Convolution Model
1.5 Classification of Seismic Inversion
1.5.1 Post-stack Seismic Inversion
1.5.2 Pre-stack Seismic Inversion
1.6 Local Optimization Methods
1.7 Global Optimization Methods
1.8 Geostatistical Inversion
References
2 Seismic Data Handling
2.1 Conditioning of Data
2.1.1 Band-Pass Filtering
2.1.2 Muting
2.1.3 Super Gather
2.1.4 Parabolic Radon Transform
2.1.5 Trim Statics
2.2 Seismic Horizons
2.3 Seismic Wavelets
2.3.1 Zero Phase Wavelet
2.3.2 Minimum Phase Wavelet
2.3.3 Extraction of Wavelet
2.4 Low-Frequency Model
2.4.1 Kriging
2.4.2 Cokriging
2.4.3 Inverse Distance Weighting (IDW)
2.4.4 Generation of Model
References
3 Post-stack Seismic Inversion
3.1 Introduction
3.2 Statistical Parameters
3.3 Band-limited Inversion
3.3.1 Application of BLI to Synthetic Data
3.3.2 Application of BLI to Real Data
3.4 Colored Inversion (CI)
3.4.1 Application of CI to Real Data
3.5 Model-based Inversion (MBI)
3.5.1 Generalized Linear Inversion (GLI) Method
3.5.2 Seismic Lithologic Modelling (SLIM)
3.5.3 Application of MBI to Synthetically Generated Data
3.5.4 Application of MBI to Real Data
3.6 Sparse Spike Inversion
3.6.1 Maximum Likelihood Inversion (MLI)
3.6.2 Linear Programming Inversion (LPI)
References
4 Pre-stack Inversion
4.1 Introduction
4.2 Simultaneous Inversion
4.2.1 Post-stack Inversion for P-Impedance
4.2.2 Extension to Pre-stack Inversion
4.2.3 Lambda-Mu-Rho (LMR) Transform
4.2.4 Application of Simultaneous Inversion
4.3 Elastic Impedance Inversion
4.3.1 Application of Elastic Impedance Inversion
References
5 Amplitude Variation with Offset (AVO) Inversion
5.1 Introduction
5.2 Fluid Replacement Modeling
5.2.1 Gassmann’s Equations
5.2.2 Fluid Properties
5.2.3 Matrix Properties
5.2.4 Frame Properties
5.2.5 Application of FRM
5.3 AVO Modeling
5.3.1 Practical Aspect of FRM
5.3.2 Direct Hydrocarbon Indicator
5.3.3 Synthetic Modeling of AVO from Logs
5.4 Amplitude Variation with Offset (AVO) Analysis
5.4.1 Classification of AVO
5.4.2 Theoretical Aspect of AVO
References
6 Optimization Methods for Nonlinear Problems
6.1 Introduction
6.2 Fitness Functions
6.3 Local Optimization Methods
6.3.1 Steepest Descent Method
6.3.2 Conjugate Gradient Method
6.3.3 Newton Method
6.4 Global Optimization Methods
6.4.1 Genetic Algorithm (GA)
6.4.2 Simulated Annealing (SA)
References
7 Geostatistical Inversion
7.1 Introduction
7.2 Seismic Attributes
7.2.1 Classification of Seismic Attributes
7.3 Single Attribute Analysis (SAA)
7.4 Multi-attribute Regressions (MAR)
7.4.1 Determining Attributes by Stepwise Regression
7.4.2 Application of MAR
7.5 Neural Network Techniques
7.5.1 Multi-layer Feed Forward Neural Network (MLFN)
7.5.2 Training and Generalization of MLFN
7.5.3 Application of MLFN
7.5.4 Probabilistic Neural Network (PNN)
7.5.5 Application of PNN
References