Geoscientist Artificial Intelligence

Geoscientist Artificial IntelligenceGeoscientist Artificial IntelligenceGeoscientist Artificial Intelligence

Geoscientist Artificial Intelligence

Geoscientist Artificial IntelligenceGeoscientist Artificial IntelligenceGeoscientist Artificial Intelligence
  • Home
  • AI Signal Processing
    • Calibration & Validation
    • Deconvolution
    • Inverse Q Filtering
    • Noise Attenuation
    • Multiple Attenuation
    • Static Correction
  • AI Imaging
    • Velocity & NMO Analysis
    • Anisotropy Analysis
    • Time to Depth Convrsion
    • Stacking
    • Migration
    • Wave Equation Datuming
  • AI Seismic Modeling
    • Ray Tracing
    • Waveform Modeling
  • AI Reservoir Characterize
    • AI INVERSION
    • AI AVO Analysis
    • Rock Physics Modeling
  • AI Depth Conversion(Dvlp)
    • Multi-Component Analysis
    • Time-Depth Relationships
    • Well Log Integration
    • Seismic Interpretation
    • Uncertainty Analysis
    • Advanced Computaion Tech
  • AI Data Integration(Dvlp)
    • Gravity and Magnetic Data
    • Electromagnetic (EM)
    • Advaned Data Fusion
  • AI FWI(Dvlp)
    • Modeling and Simulation
    • Regularized & Constraints
    • Model Parameterization
    • Other Data Integration
    • Anisotropy & Attenuation
  • More
    • Home
    • AI Signal Processing
      • Calibration & Validation
      • Deconvolution
      • Inverse Q Filtering
      • Noise Attenuation
      • Multiple Attenuation
      • Static Correction
    • AI Imaging
      • Velocity & NMO Analysis
      • Anisotropy Analysis
      • Time to Depth Convrsion
      • Stacking
      • Migration
      • Wave Equation Datuming
    • AI Seismic Modeling
      • Ray Tracing
      • Waveform Modeling
    • AI Reservoir Characterize
      • AI INVERSION
      • AI AVO Analysis
      • Rock Physics Modeling
    • AI Depth Conversion(Dvlp)
      • Multi-Component Analysis
      • Time-Depth Relationships
      • Well Log Integration
      • Seismic Interpretation
      • Uncertainty Analysis
      • Advanced Computaion Tech
    • AI Data Integration(Dvlp)
      • Gravity and Magnetic Data
      • Electromagnetic (EM)
      • Advaned Data Fusion
    • AI FWI(Dvlp)
      • Modeling and Simulation
      • Regularized & Constraints
      • Model Parameterization
      • Other Data Integration
      • Anisotropy & Attenuation
  • Home
  • AI Signal Processing
    • Calibration & Validation
    • Deconvolution
    • Inverse Q Filtering
    • Noise Attenuation
    • Multiple Attenuation
    • Static Correction
  • AI Imaging
    • Velocity & NMO Analysis
    • Anisotropy Analysis
    • Time to Depth Convrsion
    • Stacking
    • Migration
    • Wave Equation Datuming
  • AI Seismic Modeling
    • Ray Tracing
    • Waveform Modeling
  • AI Reservoir Characterize
    • AI INVERSION
    • AI AVO Analysis
    • Rock Physics Modeling
  • AI Depth Conversion(Dvlp)
    • Multi-Component Analysis
    • Time-Depth Relationships
    • Well Log Integration
    • Seismic Interpretation
    • Uncertainty Analysis
    • Advanced Computaion Tech
  • AI Data Integration(Dvlp)
    • Gravity and Magnetic Data
    • Electromagnetic (EM)
    • Advaned Data Fusion
  • AI FWI(Dvlp)
    • Modeling and Simulation
    • Regularized & Constraints
    • Model Parameterization
    • Other Data Integration
    • Anisotropy & Attenuation

AVO

 AVO stands for Amplitude Versus Offset, a technique in seismic exploration that analyzes how the amplitude of reflected seismic waves changes with source-receiver distance (offset) or equivalently, angle of incidence. It’s a powerful tool used primarily in hydrocarbon exploration to infer rock properties, fluids, and interfaces. 

 

✅ Basic Concept of AVO Modeling:

When a seismic wave reflects off an interface between two layers (with different elastic properties), the amplitude of the reflected wave changes depending on the angle of incidence. AVO analysis models this amplitude variation to extract geological information.


 

🎯 Why AVO is Important:

  • To distinguish gas/oil/brine in reservoirs
     
  • To estimate elastic properties: acoustic impedance (AI), shear impedance (SI), Poisson's ratio
     
  • To detect lithology and fluid content
     
  • To identify bright spots or flat spots

 

📈 AVO Classifications:

Based on the behavior of amplitude with offset, anomalies are classified:

  • Class I: High impedance contrast (amplitude decreases)
     
  • Class II: Zero-crossing amplitude
     
  • Class III: Low impedance contrast, amplitude increases with offset (common gas sands)
     
  • Class IV: Phase reversal or unusual gradient

AVO modeling in well location shows how the amplitude would be increased with angles around 1000-1600ms.

AVO Attributes

 

AVO (Amplitude Versus Offset) attributes are key indicators derived from the analysis of how seismic reflection amplitudes change with increasing offset or angle of incidence. These attributes provide insights into the elastic properties of subsurface rocks and are essential for identifying potential hydrocarbon reservoirs. 

🔹 1. Fundamentals

  • Definition: AVO is the variation of seismic reflection amplitude with changing source–receiver offset (or angle of incidence).
  • Purpose: Helps estimate subsurface rock and fluid properties beyond conventional reflection strength.
  • Based on Zoeppritz equations, which describe reflection/transmission coefficients as a function of angle, P-wave velocity, S-wave velocity, and density.

🔹 2. Approximations

  • Shuey’s 2-term/3-term approximation (common in practice):
     
    • R(θ)=A+B sin(2θ)+C(tan(2θ)−sin(2θ))
      where R is reflection coefficient and θ is incident angle.
      • A → Intercept (zero-offset reflectivity, related to acoustic impedance contrast).
      • B → Gradient (change of amplitude with angle, sensitive to Poisson’s ratio/fluids).
      • C → Curvature (higher angles, useful in gas sands).

1.  Intercept (A):

  • Description: The intercept represents the reflection amplitude at zero offset (or the near-offset trace). It is a measure of the baseline reflection strength and is primarily influenced by the contrast in acoustic impedance between two layers.


2.  Gradient (B):

  • Description: The gradient measures the rate of change in reflection amplitude with offset. It is sensitive to the Poisson's ratio contrast between layers, which can indicate the presence of gas or other fluids.


3.  Curvature (C):

  • Description: Curvature, or the second-order term, captures the non-linear changes in amplitude with offset. It is often used in more detailed AVO analyses to refine interpretations, especially in complex geological settings.


4.  Fluid Factor:

  • Description: This attribute is derived from the combination of intercept and gradient, designed to highlight fluid effects (like gas saturation) in the subsurface. It helps differentiate between hydrocarbon-bearing and water-bearing formations.


5.  Product of Intercept and Gradient (A*B):

  • Description: This product emphasizes areas where both intercept and gradient are strong, which can be indicative of gas sands or other significant subsurface anomalies.

Applications of AVO Attributes:

AVO attributes are critical in seismic interpretation for detecting and characterizing hydrocarbons. By analysing these attributes, geoscientists can identify gas, oil, or water-bearing formations, distinguish between different rock types, and assess the risk of drilling dry wells. They are commonly used in conjunction with AVO inversion and other seismic attributes to enhance subsurface understanding and optimize exploration and development efforts.

4o 

Application of AI AVO analysis to extract AVO attributes on real seismic data.

AVO classification

Amplitude Versus Offset (AVO) analysis is a seismic interpretation technique used to examine how the amplitude of seismic reflections changes with the distance (offset) between the seismic source and receivers. This method is particularly useful in identifying subsurface fluid content, lithology, and porosity variations, making it a powerful tool in hydrocarbon exploration.


In AVO analysis, the variation in reflection amplitude with offset is analyzed to detect changes in rock properties, such as acoustic impedance and shear modulus. Different types of AVO responses (e.g., Class I, II, III, IV) can indicate the presence of hydrocarbons or distinguish between gas, oil, and water-bearing formations. For example, a strong increase in amplitude with offset might suggest a gas-filled reservoir, while other patterns can indicate different fluid types or lithologies.


By integrating AVO analysis with other seismic attributes and well data, geoscientists can improve the accuracy of reservoir characterization, enhance the identification of potential hydrocarbon zones, and reduce the risk of drilling non-productive wells. AVO analysis is a critical component of modern seismic interpretation, providing valuable insights into the subsurface that go beyond traditional amplitude-based methods.


AVO (Amplitude Versus Offset) classification is a method used to categorize the behaviour of seismic reflection amplitudes as a function of offset or angle. This classification helps geoscientists interpret subsurface properties and identify potential hydrocarbon reservoirs. AVO classification typically divides the responses into different classes based on the changes in amplitude with increasing offset, which can indicate different types of subsurface materials and fluid contents.

Common AVO Classes

 1.  Class I:

  • Description: Characterized by a strong positive reflection amplitude at near offsets, which decreases with increasing offset.
  • Indication: Typically represents high-impedance contrasts, often associated with gas sands overlain by denser rocks like shales.

2.  Class II:

  • Description: Displays a near-zero or weak positive amplitude at near offsets that increases slightly or stays flat with increasing offset.
  • Indication: Often indicates gas sands with minimal impedance contrast relative to the surrounding rock.

3.  Class IIp (Polarized):

  • Description: A variant of Class II, where the amplitude is negative at near offsets and becomes positive at far offsets.
  • Indication: Suggests a small impedance contrast that reverses polarity with increasing offset, often seen in gas-filled sands.

4.  Class III:

  • Description: Characterized by a negative reflection amplitude at near offsets that becomes more negative with increasing offset.
  • Indication: Typically indicates low-impedance contrasts, such as gas sands with significant contrast to the overlying shales.

5.  Class IV:

  • Description: Displays a negative reflection amplitude at near offsets that decreases (becomes less negative) with increasing offset.
  • Indication: Represents a weak negative impedance contrast, often associated with certain types of wet sands or low gas saturation.

Applications of AVO Classification

AVO classification helps distinguish between different lithologies and fluid types in the subsurface. By analysing the AVO response, geoscientists can identify prospective hydrocarbon zones, differentiate between gas and oil reservoirs, and reduce the risk of drilling dry wells. It is particularly valuable in exploring subtle traps and unconventional reservoirs, where traditional seismic interpretation might not provide clear indicators.

Application of AI AVO classification in selected zone in the reservoir.  It seems some suit purple point with class type I in chart.

Copyright © 2025 Geoscientist Artificial Intelligent - All Rights Reserved.

Powered by

This website uses cookies.

We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.

Accept