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

rock physics modeling (RPM)

Rock Physics Modeling (RPM) is the discipline that establishes the quantitative link between the geological and petrophysical properties of rocks and their elastic properties, which ultimately control the seismic response. In seismic reservoir characterization, seismic data provide information only about acoustic impedance contrasts, which are functions of compressional velocity, shear velocity, and density. However, the main exploration and production objectives are lithology, porosity, and fluid content. RPM serves as the physical bridge that translates between these domains.

The foundation of RPM lies in describing how mineral composition, porosity, fluid saturation, pressure, and temperature affect elastic parameters such as VpV_pVp​, VsV_sVs​, and density. These elastic parameters are then used to predict seismic attributes such as acoustic impedance, shear impedance, Vp/Vs ratio, and Poisson’s ratio. The modeling process involves both empirical and theoretical frameworks. Empirical relations include Gardner’s density–velocity relationship and Castagna’s mudrock line, while theoretical approaches rely on Gassmann’s equations for fluid substitution and effective medium theories for pore structure, cementation, and fractures.

In practice, RPM begins with well log and core data, which provide the necessary calibration for elastic property prediction. Once calibrated, RPM allows the simulation of fluid substitution scenarios, for example, predicting how the seismic response changes when brine is replaced by oil or gas. It also enables the forward modeling of synthetic seismic and amplitude variation with offset (AVO) responses, which can then be compared with field seismic gathers. A key application of RPM is the construction of Rock Physics Templates (RPTs), which are crossplots of elastic properties under varying lithology and fluid conditions, and which serve as diagnostic tools for lithology and fluid discrimination.

Ultimately, RPM provides the essential framework for linking petrophysical properties to seismic observations, thereby enabling robust seismic inversion, AVO analysis, and quantitative reservoir characterization. Without this physics-based connection, seismic amplitudes could not be reliably interpreted in terms of subsurface geology or fluid content.


Rock Physics Modeling (RPM) is the science and practice of linking geological and petrophysical properties of rocks (porosity, mineral composition, fluid content, pressure, temperature) to their elastic properties (P-wave velocity, S-wave velocity, and density), which in turn control the seismic response.

In other words:     👉 RPM is the bridge between rocks in the subsurface and seismic data at the surface.

Core Steps in Rock Physics Modeling

 

1. Well Log Conditioning 

  • Clean and depth-match sonic, density, resistivity, neutron logs.
  • Remove bad hole effects.

2. Elastic Property Computation 

  • Compute P-wave velocity (Vp), S-wave velocity (Vs), density (ρ).
  • Derive elastic moduli: 
    • Bulk modulus (K), Shear modulus (μ), Poisson’s ratio (ν).

3. Fluid Substitution (Gassmann’s Equation) 

  • Replace brine with gas/oil/water.
  • Study how saturation changes seismic velocities.

4. Rock Physics Templates (RPTs) 

  • Crossplot Vp/Vs vs Acoustic Impedance or Poisson’s Ratio vs Impedance.
  • Separate lithology (sand, shale, limestone) and fluids (oil, gas, brine).

5. AVO Modeling 

  • Use Zoeppritz or Shuey equations with modeled elastic properties.
  • Generate synthetic angle gathers.
  • Analyze intercept (A), gradient (B), and curvature (C).
6. Forward Modeling & Feasibility 
  • Simulate seismic responses (wavelet convolved).
  • Check if gas sands stand out compared to brine sands and shales.
7. Advanced Topics
  • DEM (Differential Effective Medium theory) for modeling cementation/porosity reduction. 
  • Hudson’s crack theory to model anisotropy from aligned fractures.  
  • Biot-Gassmann theory for frequency-dependent fluid effects.  
  • Probabilistic rock physics inversion using Bayesian frameworks connecting seismic amplitudes to lithology/fluid facies.

Key Outputs of Rock Physics Modeling

  •  Synthetic AVO curves for different lithology-fluid combinations.
  • Crossplots (like AVO class crossplots, RPTs).
  • Feasibility analysis: Can seismic actually detect fluid/lithology change?
  • AVO Class Prediction: Gas sands → often Class III, tight sands → Class I/II.

rock physics modeling (RPM)

Rock Physics Modeling (RPM) in synthetic model with following parameters to investigate the behavior of oil,brine and gas in the reservoir.

% Define a simple 4-layer model (vectors)

layers thickness    = [500 350 400 350];   % m

layers P-wave velocity     = [3000 2600 3200 2800]; % m/s

layers S-wave velocity     = [1600 1400 1700 1500]; % m/s

layers Density    = [2.35 2.15 2.40 2.25]; % g/cc

layers Porosity    = [0.15 0.22 0.12 0.18];

layers Matrix compress ability (Km)     = [36.6 36.6 36.6 36.6]; % quartz-like

layers state  = saturated

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