Rock Physics Modeling and Quantitative Interpretation (QI)

Rock physics serves as a crucial bridge between seismic inversion results (Vp, Vs, Density) and reservoir properties (Porosity, Vshale, Saturation). subsurfaceAI 2024 enhances this connection by providing easy-to-use workflows for building rock physics templates (RPT). These workflows allow users to select preferred rock physics models that honor local geology and well data, facilitating the estimation of porosity, Vshale, lithofacies, and oil/gas saturation from seismic inversion volumes.

Comprehensive Rock Physics Modeling

SubsurfaceAI 2024 offers a robust suite of rock physics models for building accurate and tailored RPTs. These models include:

  • Gassmann’s Equations
    • Fluid substitution in porous rocks.
    • Relates the bulk modulus of saturated rock to the moduli of the rock frame, pore fluid, and mineral matrix.
  • Biot-Gassmann Theory
    • Extended for complex pore geometries and different frequencies.
    • Integrates the low-frequency limit (Gassmann) and high-frequency limit (Biot).
  • Biot’s Theory of Poroelasticity
    • Describes wave propagation in fluid-saturated porous media.
    • Combines fluid and solid mechanics to predict attenuation and dispersion.
  • Kuster-Toksöz Model
    • Effective elastic moduli of a rock with inclusions.
    • Considers the shape and distribution of inclusions (e.g., pores, cracks).
  • Voigt-Reuss-Hill Averages
    • Upper and lower bounds on elastic moduli.
    • Voigt average assumes uniform strain, Reuss average assumes uniform stress, and Hill average is the arithmetic mean.
  • Hashin-Shtrikman Bounds
    • Theoretical bounds on effective elastic moduli.
    • Accounts for phase properties and volume fractions.

Empirical Relationships

  • Gardner’s Relation
    • Density and P-wave velocity relationship.
    • Used for lithology and porosity estimation.
  • Wyllie’s Time Average Equation
    • Seismic velocity related to porosity and fluid content.
    • Assumes a linear relationship between travel time and porosity.

Fluid and Saturation Models

  • Patchy Saturation Models
    • Heterogeneous fluid distribution effects on seismic velocities.
    • Includes models like White’s theory of partial saturation.
  • Batzle-Wang Model
    • Empirical relationships for pore fluid properties.
    • Provides fluid density and bulk modulus based on temperature, pressure, and composition.

Complex Moduli Models

  • Voigt-Reuss-Hill Averages for Anisotropic Media
    • Extends Voigt-Reuss-Hill to anisotropic materials.
  • Geertsma-Smit Model
    • Predicts bulk modulus of a rock with a given pore structure.
    • Considers pore shape and orientation.

Advanced and Hybrid Models

  • DEM-Gassmann Hybrid Models
    • Combines Differential Effective Medium theory with Gassmann’s equations.
    • Useful for rocks with mixed pore geometries and fluid content.
  • Xu-White Model
    • Empirical and theoretical approach for clastic sediments.
    • Models velocity and density based on clay content, porosity, and mineralogy.
  • Ciz-Dvorkin Model
    • Extends rock physics models for different cement types and contact mechanics.
    • Useful for carbonate reservoirs.

Deliver Reservoir Facies

Deliver Reservoir Facies based on Seismic Inversions and Rock physics Templates

subsurfaceAI 2024 allow you to overlay a rock physics template (RPT) to the 2-D cross plot as the classification polygons, leveraging the powerful 2-D cross plotting classification functions. You can then generate facies or property grids and maps based on the RPT and seismic inversion plotted on the cross plot.

Data-Driven Approaches

Data-Driven Approaches for Quantitative Interpretation

In addition to powerful rock physics modeling functions, subsurfaceAI 2024 also provides data-driven approaches for QI using machine learning and geostatistical methods. This combination of traditional rock physics models and advanced data-driven techniques allows for comprehensive and accurate quantitative seismic interpretation, enabling better reservoir characterization and decision-making.

subsurfaceAI 2024 is your comprehensive solution for leveraging rock physics modeling, geostatistics, and machine learning in quantitative seismic interpretation, providing unparalleled insights into reservoir properties.

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