Seismic Data Conditioning, AVO Modeling and Analysis, and Inversion for Reservoir Characterization

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Learning Objectives
Upon completion of this course, participants will grasp key aspects of the concept, workflow and benefits of predicting fluid and rock properties and estimating uncertainties for a better understanding of the reservoir.
Audience
Anyone interested in understanding the theory, workflow and optimal way to integrate reservoir properties into accurate and predictive models.
Content
Day 1 – Data Conditioning
- Random and coherent noise attenuation
- Residual NMO and gather alignment
- Amplitude correction
- Frequency and bandwidth
- Phase distortion
- Trace organization and conversion
- 4D seismic conditioning
- Common offset/angle Azimuthal Gathers
Day 2 – AVO Modeling and Analysis
- Introduction: Basic seismic wave principles, Poisson's ratio, gas saturation, Biot-Gassmann equations, and lithologic examples
- AVO Theory: Zoeppritz's equations, Aki-Richards and Shuey's approximation, elastic wave modeling, and impact of anistropy
- AVO Analysis: AVO attribute volume generation and considerations, processing concerns, interpretation of AVO measurements, classification of AVO responses, AVO crossplotting and polarization, and examples from published case studies
- Emphasizes practical AVO examples from a number of regions around the world
- Explains both the advantages and potential pitfalls of the AVO method
Day 3 – Deterministic Inversion using Strata
- Introduction: Convolutional models, wavelets, reflectivity and noise
- Theory: Recursive, sparse-spike, model-based and colored inversion. Pre-stack methods of Elastic Impedance and Lambda-Mu-Rho
- Analysis: Seismic and wavelet processing, amplitude recover, noise attenuation and imaging
- Practical: Examples of band-limited, sparse-spike and model-based inversion
- Includes pre-stack Simultaneous Inversion
Day 4 – Geostatistical Inversion using GeoSI
- Introduction to inversion methods – deterministic and stochastic.
- Log correlation and Model Building on both seismic and stratigraphic grids
- Basic stochastic inversion theories: Sequential Gaussian Simulation, Bayesian Stochastic inversion and GeoSI inversion theory
- Correlation and Variogram modelling
- Facies classification theory
- Stochastic lithology prediction
- Stabilizing the results
- 3D visualization
Duration: 3 1/2 days
Software used: HampsonRussell AVO, Strata, GeoSI
Course Format: Instructor-led, workflow-based, virtual classroom
Instructor(s): Shervin Rasoulzadeh
Prerequisites: None
Number of Participants: 8-10
Price:
US $3,000.00
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