This course reviews various physical properties of rocks and fluids and the seismic response to materials with those properties with direction applications to exploitation, exploration, and geophysical modeling.

Course Outline

1. Introduction and basic geophysical concepts
2. Stress, strain, and elasticity of porous media
3. Factors that affect seismic velocity
4. Reservoir fluid properties
5. Effective medium models for rocks
6. Gassmann’s equation and fluid effects on elastic properties
7. Solid substitution of rock constituents
8. Seismic properties of sand-clay clastic reservoir rocks and soft sediments
9. Fluid substitution in thinly-bedded reservoirs
10. Effects of saturation and scale on geophysical properties
11. Carbonates
12. AVO and interpretation templates
13. Fluid flow in porous rocks
14. Velocity dispersion and attenuation
15. Anisotropy and seismic signatures of fractured rock
16. A shale r

Duration: Five days

Intended Audience: Entry and Intermediate

Prerequisites (Knowledge/Experience/Education Required)

This course is for geologists, geoscientists, and geophysicists seeking to learn and understand the information content and value contained in velocity model building for depth imaging to enhance seismic depth imaging quality. The basic knowledge about seismic data acquisition, processing, seismic depth imaging, Unix system, and linear algebra is desirable but not required.

Course Outline

The goals of this course are: (1) to learn and understand, in an intuitive way, the basic concepts and practical aspects of velocity model building used for an optimal seismic depth imaging, and (2) to expose the participant to current velocity model building practices used by geophysicists and geoscientists through workflows and tutorial examples.

General Course Contents

Day 1

1. Lecture: Introduction to the course
2. Lecture: Overview of velocity model building techniques
3. Lab: Learning Unix system and setting up of personal Unix account
4. Lab: Learning Seismic Unix

Day 2

1. Lecture: Kirchhoff depth migration
2. Lab: practice of implementing Kirchhoff depth migration
3. Lecture: Constructing input of velocity model building by tomographic inversion
4. Lab: Practice of picking and QC residual depth errors

Day 3

1. Constructing operator of tomographic inversion]
2. Lab: Practice of generating matrix for tomographic inversion
3. Lecture: Solving velocity update
4. Lab: Practice of generating velocity update

Day 4

1. Lecture: Post-tomographic inversion analysis
2. Lab: Practice of analyzing and QC tomographic results

Day 5

1. Lecture: Full Wave Inversion
2. Lecture: Road ahead in velocity model building

Instructor Biography of Jonathan Liu

Dr. Jonathan Liu graduated in 1995 from Colorado School of Mines, Center for Wave Phenomena (CWP). He joined ExxonMobil Corporation as a research geophysicist since his graduation. During his 26 years of career in oil industry, Jonathan focused on research and development of methods and workflows on seismic imaging and velocity model building. Jonathan retired on December 1, 2021, and began to work in University of Houston, Department of Earth and Atmospheric Sciences, as an Adjunct Professor.

A seismic attribute is any measure of seismic data that helps us better visualize or quantify features of interpretation interest. Seismic attributes fall into two broad categories – those that help us quantify the morphological component of seismic data and those that help us quantify the reflectivity component of seismic data. The morphological attributes help us extract information on reflector dip, azimuth, and terminations, which can in turn be related to faults, channels, fractures, diapirs, and carbonate buildups. The reflectivity attributes help us extract information on reflector amplitude, waveform, and variation with illumination angle, which can in turn be related to lithology, reservoir thickness, and the presence of hydrocarbons.

A human interpreter integrates the images provided by seismic attributes with well log and production data using an appropriate tectonic, stratigraphic, and/or diagenetic geologic models to infer lithology, porosity, and fractures and other features of interest. Because attributes quantitively measure the smaller-scale features and patterns seen by human interpreters, attributes provide critical input to modern machine learning analysis tools.

In this course, we will gain an intuitive understanding of the kinds of seismic features that can be identified by 3D seismic attributes, the sensitivity of seismic attributes to seismic acquisition and processing, and how ‘independent’ seismic attributes can be coupled through geology. Having learned the properties that each attribute measures, we will be able to choose appropriate attribute candidates for machine learning analysis to predict seismic facies, and when abundant well control exists, areas of better production, and zones of more costly drilling.

Although seismic attributes are well implemented in all of the larger interpretation software packages, licenses to such software is not generally available for use in public professional society courses. Geoscientists between jobs or engineering, data analytics, and other professionals wanting to move into geosciences may not have access to any interpretation software. To address this shortcoming participants will be provided an evaluation license of the AASPI software (described under http://mcee.ou.edu/aaspi/documentation.html) for their desktop or laptop to carry out the class exercises, and ideally, to apply to their own data volumes.

Course Outline

1. Introduction: An overview of how seismic attributes fit within modern interpretation workflows.
2. Complex trace, horizon, and formation attributes: Theory, definition, and limitations of attribute based on the analytic (or complex trace) such as envelope and instantaneous frequency. Definition and use of attributes computed from a horizon, such as dip magnitude and horizon-based curvature as well as formation attributes computed between horizons, such as RMS amplitude and thickness.
3. Color and multiattribute display: Definition and interrelationship between RGB, CMY, and HLS color models. Best practices for multiattribute display.
4. Spectral decomposition and thin-bed tuning: Theory, workflows, and advantages of the three most commonly used spectral decomposition algorithms (DFT, CWT, and matching pursuit). Their use in mapping "tuned" geologic features that fall at or below seismic resolution.
5. Geometric attributes that map reflector configuration: A summary of volumetric dip and azimuth, curvature, reflector shapes, aberrancy, and reflector convergence (or nonparallelism).
6. Geometric attributes that map continuity and textures: A summary of coherence, multispectral coherence, amplitude or energy gradients and curvature, and gray level coocurrence matrix texture attributes.
7. Multispectral, multioffset, and multiazimuth coherence: The value of the separate and combined analysis of coherence and other attributes computed on separate and/or multiple input volumes.
8. Attribute expression of tectonic deformation: Attribute expression of faulting and folding as seen on post stack volumes by coherence, curvature, and reflector rotation.
9. Attribute expression of clastic depositional environments: Attribute expression of fluvial/deltaic and deepwater systems as seen on post stack volumes by spectral decomposition, coherence, curvature, and refector convergence attributes. Attribute expression of differential compaction.
10. Attribute expression of carbonate deposition environments: Attribute expression of carbonate buildups and diagenesis as seen on post stack volumes by coherence, curvature, and texture attributes. Attribute expression of karst terrains.
11. Attribute expression of shallow stratigraphy and drilling hazards: Attribute expression of mass transport complexes, glide tracks, outrunner blocks, pock marks, glacial keel marks,and shale "dewatering" (syneresis) features, many of which when gas- or water-charged may become drilling hazards.
12. Attribute expression of igneous instrusion, extrusions, and basement: Attribute expression of volcanic mounds, sills, fractured basement, and lacoliths which can serve as or give rise to reservoirs. Impact of overlying igneous rocks on seismic data quality.
13. Seismic chronostratigraphy: An overview of workflows that generate 100s of horizons within a given formation or stratigraphic package; the use of Wheeler diagrams and simple thickness and RMS attributes to map areas of greater accomodation and deposition vs erosion.
14. Impact of acquisition and processing on seismic attributes: Value of long-offset, wide-azimuth, and dense seismic surveys in seismic data quality and attribute analysis.
15. Direct and indirect estimates of fractures and horizontal stress: Use of curvature, impedance, and seismic anisotropy to map the orientation and intensity of natural fractures and/or horizontal stress. Calibration with lidar data and image logs. Measurement and interpretation of azimuthal anisotropy volumes.
16. Poststack seismic data conditioning: Spectral balancing, structure-oriented filtering and footprint suppression of poststack data volumes.
17. Prestack seismic data conditioning: Prestack structure-oriented filtering, nonhyperbolic moveout, and correction of NMO/migration stretch. Preconditioned least-squares migration and 5D interpolation.
18. Image enhancement and object detection: Algorithms that enhance faults and channels to generate computer "objects". Lay-person's explanation of modren ant-tracking, skeletonization, and level set algorithms that indicate the future of computer-assisted seismic interpretation.
19. Visual decision making and interactive multiattribute analysis: Review of multiattribute display, crossplotting, and geobodies. Principal component and independent component analysis dimensionality reduction techniques.
20. Unsupervised multiattribute classification: Clustering algorithms including k-means, self-organizing maps (e.g. Stratimagic's "waveform classification") and generative topographic maps.
21. Supervised multiattribute classification: Artificial neural networks, probabilistic neural networks, random forest desicison trees, and support vector machine algorithms. The value of unsupervised vs. supervised neural networks.
22. The requirements of future E&P data integration - Shallow learning but deep thinking? Or Deep learning and shallow thinking: An overview of deductive reasoning as used in human-driven interpretation and inductive reasoning more common to conventional statistical analysis and more modern machine learning algorithms.

Who should attend?

• Seismic interpreters who want to extract more information from their data.
• Seismic processors and imagers who want to learn how their efforts impact subtle stratigraphic and fracture plays.
• Sedimentologists, stratigraphers, and structural geologists who use large 3D seismic volumes to interpret their plays within a regional, basin-wide context.
• Reservoir engineers whose work is based on detailed 3D reservoir models and whose data are used to calibrate indirect measures of reservoir permeability.

Advanced knowledge of seismic theory is not required; this course focuses on understanding and practice.

Kurt Marfurt is an Emeritus Professor of Geophysics at the University of Oklahoma, where he mentors students and conducts research to aid seismic interpretation. Marfurt holds M.S. and Ph.D. degrees in Applied Geophysics from Columbia University in the City of New York and an A.B. in Physics and French from Hamilton College.

Marfurt experience includes 25 years as an academician, first at Columbia University, then later at the University of Houston and the University of Oklahoma. His career also includes 18 years in technology development at Amoco’s Tulsa Research Center working on a wide range of topics including seismic modeling, seismic imaging, VSP analysis, signal analysis, magnetotellurics, basin analysis, seismic stratigraphy, and seismic attributes. At OU, Marfurt led the Attribute-Assisted Seismic Processing and Interpretation (AASPI) consortium with the goal of developing and calibrating new seismic attributes to aid in seismic processing, seismic interpretation, and data integration using both interactive and machine learning tools. With colleagues, he has received several best paper and best presentation awards on seismic modeling, coherence, curvature, principal component analysis, and brittleness estimation. He is an honorary member of the SEG, in 2019 received the AAPG Robert R. Berg outstanding research award, and in 2023 the SEG Maurice Ewing medal. Marfurt served as the 2006 EAGE/SEG and as the 2018 SEG Distinguished Short Course Instructor. He has taught continuing education short courses for the SEG and AAPG since 2003. From 1984-2013, he served as either an associate or assistant editor for Geophysics. In 2013 he joined the editorial board of the SEG/AAPG journal Interpretation where he served as the Editor-in-Chief for 2016-2018, and subsequently as Deputy Editor-in-Chief for 2019-2021. He served as a Director-at-Large for the SEG from 2019-2022. Currently, he is writing a new book with Satinder Chopra on seismic attributes.

All rock masses are seismically anisotropic, but we often ignore this in our seismic acquisition, processing, and interpretation. The anisotropy nonetheless does affect our data, in ways that limit our effectiveness in using it, if we ignore this basic fact. This course helps us understand why this inconsistency between reality and practice has been so pervasive in the past and why it will be less successful in the future, as we acquire modern seismic data (especially including wide-azimuth and vector seismic data) and as our management has correspondingly higher expectations of it (and of us). This course helps us understand how we can modify our practice to realize more fully the potential inherent in our data, through algorithms which recognize the fact of seismic anisotropy.

The course offers a five-day immersion in the important ideas of seismic anisotropy, across the entire spectrum of applications, starting with fundamental ideas, and applying these to P-wave subsurface imaging and physical characterization, then to vector waves (S-waves and C-waves). It shows how isotropic seismic is just an elementary introduction to a much richer range of phenomena. Although seismic anisotropy is usually weak (<10%, as measured by elastic constants), it can have surprisingly large effects (> 100%) in some contexts and causes completely new effects in other contexts. These ideas assume particular importance for those geophysicists involved in the shale resource play.

Although 95+% of our data these days is P-wave data, the vector wave discussion is nevertheless important (recall the appearance of the shear modulus in the AVO equation). Further, we will encounter these datatypes even more in the future, and, as you will see, the isotropic model is hopelessly naïve for such waves.

Numerous Excel exercises help students understand these ideas through their own hands. Some of these exercises offer computational functionality that is unavailable at most corporations.

Course Outline

1. Physical principles (Day 1)
2. P-waves: imaging (Day 2)
3. P-waves: characterization (Day 3)
4. S-waves: (Day 4)
5. C-waves: (Day 5)
6. Epilogue: (Day 5)

This course provides a thorough overview of the methods of multi-component (3-C and 4-C) or full-wave seismic exploration from basic petrophysical analysis and survey design through elastic-wave analysis to time-lapse applications. The emphasis is on converted seismic waves and their use. Numerous examples and case histories illustrate the design, application, and use of multi-component seismic values and images. In situ measurements and analysis (from well logs and borehole seismic) are first treated using a variety of well logs and 3-C VSP. Newer technologies such as fiber-optic sensors, microseismic imaging, and ocean-bottom nodes are assessed. Considerable discussion is focused on analysis techniques, including anisotropy and attenuation aspects, for the borehole and surface seismic data. We undertake integration of the various data sets using geostatistical techniques. Exercises to practice the methods of analysis and interpretation of the PP and PS data reinforce concepts introduced.

Benefits and Learning Goals

The course will benefit geoscientists seeking to learn more about advanced seismic methods as well as ways to enhance broad exploration in addition to detailed reservoir imaging and interpretation. The course will present the elements of full-wave (multicomponent, vector, elastic) seismic methods (theory, acquisition, processing, interpretation) and students will gain appreciation where the methods can be used and bolster their facility in using rock physics, logs, VSP, and 3C/4C/9C seismic data. Upon completing the course, participants will have familiarity with and skills using borehole and surface multicomponent seismic methods, especially converted-wave technology, and their use in a variety of resource-related applications.

Part 1 - Petrophysics, well logs, and 3C borehole seismic methods

1. Overview of course (topics and objectives)
2. Core and well log analysis
3. Fundamentals of elastic-wave propagation
4. Borehole seismic measurement, processing, interpretation
5. Microseismic, crosswell, and special cases

Part 2 - Fundamentals of multicomponent surface seismic methods

1. Basic multicomponent instrumentation, survey design, and acquisition
2. Time domain processing of PS data for land and OBN including noise identification and removal, signal enhancement, statics
3. Model building of S-wave velocity: tomography and full waveform inversion
4. PS wave imaging (time and depth)
5. Interpretation of PP and PS images

Part 3 - Advanced multicomponent practices

1. Attribute computation, joint inversion of PP and PS.
2. Carbonate-related PS processing, Fracture analysis.
3. PS imaging and model building in anisotropic media
4. PS imaging for unconventional resources
5. Future directions

A geophysical interpretation of a seismic anomaly consists of two general components. One relates to the amplitude and the other to the spatial distribution of the anomaly. The interpretation of the amplitude validates the composition of the reservoir (reservoir characterization) while the interpretation of the spatial distribution validates the structural and stratigraphic framework (reservoir delineation). This course deals with amplitude interpretation.

Objective

At the end of the course, the participant should be able to

• Include well-log curves in seismic interpretation
• Recognize hydrocarbon signatures in different rock-property environments
• Model and interpret Amplitude Variation with Offset (AVO) synthetics
• Model seismic rock properties as a function of porosity, rock type, pore fluid
• Estimate reservoir rock type and pore fluid
• Conduct an AVO interpretation for reservoir characterization

Textbook, Notes, and Computer Programs provided by Fred Hilterman

• Hilterman, F., 2001, Seismic Amplitude Interpretation: Society of Exploration Geophysicists 2001 - Distinguished Instructor Short Course (DISC). Additional handouts provided.
• Supplement PDF articles: Geophysics, Geophysical Prospecting, TLE and First Break
• EXCEL interactive programs will compliment workshop exercises. Also, AVO modeling with fluid-substitution capabilities, numerous AVO cross plots, Zoeppritz plots, etc. are provided.

Course Content

Sec 1: Introduction

• Interpretation philosophy
• Basic amplitude-vs-offset (AVO) principle
  Ex. Quick-look AVO modeling (Wk-E)

Sec 2: Well-Log Principles for Seismic

• Basic rock nomenclature and properties
• Well-log tools & interpretation
  Ex. Formation properties from well logs (Wk-B)

Sec 3: Rock Physics

• Theoretical and empirical relationships
• Velocity and petrophysical properties
• Velocity-density models
  Ex. Velocity-porosity prediction from seed point
• Fractured medium

Sec 4: Reflection Amplitude

• Wave Propagation in Solid Medium
• Normal-incident (NI) and far-angle amplitude expressions
• Synthetics: Well ties … Internal multiples
• Interpretational aspect of internal multiples
• Amplitude versus bed geometry
  Ex. Amplitude pitfalls from 2D Acquisition (Wk-A)
• AVO equation: Rock-property correlation
• Synthetic stretch measures attenuation to improve well tie
• Weak anisotropy
• Reflectivity modeling

Sec 5: Properties of AVO anomalies

• 1970’s amplitude classification
• 1990’s amplitude classification
• Class 1-4 AVO signature examples
  Ex. Quantifying AVO signatures
• Hydrocarbon indicators

Sec 6: Amplitude ⇔ Pore fluid & rock type

• Shuey’s calibration crossplot
• Building a Shuey crossplot
• Application of Shuey crossplot
  Ex. Quick-look forward and inverse AVO modeling

Sec 7: AVO Reservoir Characterization

• Post-stack seismic attributes
• AVO reflectivity inversion
• Reflectivity attributes
• Layer attributes
  Ex. AVO Modeling – Jaguar Field (Wk-C)
• Inversion & Reconstruction well-log curves ⇒ Attribute templates
• Fracture attributes from seismic
• Sensitivity analysis: consolidated vs. unconsolidated

Sec 8: Amplitude Interpretation Case Histories

• Class 3 – Lithologic identification
• Class 3 – Ursa – Deep-water field
• Class 2 – Axis rotation and crossplotting
• Class 2 – Lithostratigraphic vs. chronostratigraphic
• Class 2 – Anisotropic NMO
• Class 1 – Anisotropic NMO

Sec 9: Final Comments, Discussion & Future Expectations

• AVO check list
• Unusual AVO anomalies
• Future challenges

Who Should Attend

The course is intended for geophysicists, geologists and engineers. The participant is assumed to have a basic understanding of the seismic process and its nomenclature; such as, what is a: CDP gather, velocity analysis, seismic horizon vs time slice, etc. The course will build on the basic understanding of seismic and at times suggest new insight for old interpretation problems; such as, quantifying amplitude loss and phase distortion based on a well-tie synthetic “stretch”. Numerous rules-of-thumb are provided to allow interpreters to quickly understand the interpretational significance of different attribute anomalies that might be observed.

This course gives an overview of marine CSEM and EM methods along with details on acquisition and processing of the data. Modeling and inversion are covered as well as the rock physics principles that facilitate the method. Applications through case studies of these tools are presented.

• Day 1:
  • AM - Introduction
  • PM - Sensitivity
• Day 2:
  • AM - Acquisition and processing
  • PM - Practical on Acquisition and Processing of CSEM and EM data (Computer Lab)
• Day 3:
  • AM - Modelling and Inversion
  • PM - Integration and case studies
• Day 4:
  • AM - Case studies
  • PM - Rock physics of resistivity
• Day 5:
  • AM - Case studies continued
  • PM - Wrap up

Seismic migration is the main subsurface imaging method in petroleum exploration based on reflection seismic data. This short course aims to examine the basic methodology and underlining geophysical principles of seismic migration. Some common migration methods, such as Kirchhoff, phase-shift, and full-wave migration, will be studied and illustrated with case studies. Relevant topics to be discussed include pre-processing of migration data and velocity model building. In particular, the dependency of migration on the velocity-depth model and migration velocity analysis will be analyzed. The course will focus on principles, practicality, and future directions of seismic migration.

Course Outline

1. Introduction to Migration: Definition and outline of methods, time processes before imaging, stacking velocities, math background.
2. Kirchhoff Migration: Kirchhoff integral, Rayleigh integral, Kirchhoff migration, travel time and ray tracing.
3. Frequency Domain Migration: Time versus frequency domains, geometrical overview of f-k migration, phase-shift migration.
4. Refraction velocity analysis: Subsurface velocity measurements and trend, deriving velocity using first arrivals for near-surface static corrections.
5. Finite Difference Migration: Approximations of up-going wave equation, retarded coordinates, finite differencing wave equations.
6. Semblance velocity analysis: Practical issues in velocity analysis associated with migration, converted wave processing and imaging.
7. Reverse time migration: Benefits and limitations for RTM using primaries as well as non-primary reflections.
8. Migration Velocity Analysis: Initial velocity model building, focusing analysis, common image gathers, and tomographic velocity analysis.
9. Advanced migration methods: Least squares migration and applications of machine learning in seismic migration and velocity model building.

Course Outline

1. Acoustic and elasticity theory, wave equations, Newton's and Hooke's laws.
2. Body waves, P and S reflections, transmission+reflection coefficients, refractions, spherical, cylindrical, diffraction and plane waves.
3. Surface waves: Rayleigh, Love, and guided waves.
4. Applications: SH and SV imaging, AVO, Up and Down separation.
5. Finite-difference solution to the wave equation and eikonal equation.
6. Ray theory, attenuation, and traveltime tomography.
7. Convolutional model of the seismogram.
8. Wave and ray theory concepts for migration and imaging.