Validate Static and Dynamic Reservoir Models Through Rock Physics Modeling

(Revised and expanded version, replacing your original Part 2 article.)

Rock Physics Modeling-Part 2

Introduction: From Concept to Physics-Based Reservoir Validation

In Part 1 of this series, we introduced the core idea that rock physics modeling (RPM) should not be a rare, specialist-only exercise, but rather a standard, everyday tool for seismic interpreters. We showed how SubsurfaceAI’s Rock Physics Modeling (RPM) module and Rock Physicist Templates (RPTs) enable interpreters—not just rock physics experts—to incorporate physics directly into well ties, AVO analysis, inversion QC, and amplitude interpretation.

Here in Part 2, we move from understanding why rock physics belongs in seismic interpretation to showing how interpreters can use RPM and RPTs to perform one of the most powerful workflows in modern subsurface characterization:

Validating static and dynamic reservoir models against real seismic data using forward rock physics and synthetic seismic modeling.

This workflow is not theoretical—it is immediately practical and extremely valuable. Modern oil and gas developments depend on accurate reservoir models for:

• Field development planning
• Production management
• Enhanced recovery design
• Reserve estimation and classification
• 4D (time-lapse) surveillance & history matching

Because these models directly affect drilling decisions, production forecasts, and multi-million-dollar investment choices, the question becomes:

How do we test whether a reservoir model is consistent with reality, not only in well space but across the entire seismic volume?

This is where SubsurfaceAI’s RPM and RPTs shine. They provide seismic interpreters with a robust, intuitive way—with no coding required—to generate elastic models and synthetic seismic from reservoir simulations and compare them directly with the real seismic volume.

This capability closes the loop between geophysics, geomodeling, and reservoir engineering. It elevates the seismic interpreter from “provider of structure maps” to a key partner in validating and improving reservoir models.

The Big Idea: If the Reservoir Model Is Right, the Seismic Should Match

At the heart of this workflow is a simple but powerful argument:

If a static or dynamic reservoir model is close to the true subsurface, then the synthetic seismic generated from that model should resemble the real seismic data.

And conversely:

If the synthetic seismic does not match the real data, the reservoir model is missing something important.

This introduces an interpretive, physics-driven loop:

1. Build rock physics models (RPTs) from wells.
2. Apply reservoir properties (porosity, Vshale, saturation, pressure).
3. Compute elastic properties (Vp, Vs, density).
4. Generate synthetic seismic from these elastic volumes.
5. Compare synthetic vs. real seismic.
6. Improve reservoir model assumptions accordingly.

This is not just a theoretical loop—it can be implemented entirely inside SubsurfaceAI, by interpreters, without any external software or coding.

This is important because traditional forward-modeling workflows:

• Require specialist tools
• Demand coding or scripts (MATLAB/Python)
• Are disconnected from interpretation environments
• Are too slow or cumbersome to perform routinely

SubsurfaceAI solves all of these issues by making forward-modeling point-and-click, integrated, and interpreter-friendly.

Part 2 Workflow Overview: Using RPM & RPTs to Validate Reservoir Models

Below is the complete workflow for validating static and dynamic reservoir models using SubsurfaceAI’s RPM/RPT capabilities.

Step 1: Build Rock Physicist Templates (RPTs) for Zones of Interest

Everything begins with the well data, because wells anchor rock physics in reality.

RPTs capture all rock physics assumptions—including mineral moduli, fluid properties, mixing rules, elastic models, porosity–velocity relationships, and saturation behavior.

For each reservoir zone or facies, you:

Gather well data

• P-sonic, S-sonic, density logs
• Porosity logs (density, neutron, phi-T)
• Vshale indicators (GR, spectral gamma)
• Fluid information (logs, core, tests)
• Core measurements (if available)

Choose the appropriate rock physics model

SubsurfaceAI includes:

• Sand–shale mixture models (Han, Castagna, modified Mudrock)
• Cemented and uncemented sand models (soft-sand/stiff-sand)
• Carbonate models
• Patchy/uniform saturation models
• Gassmann fluid substitution for fluid effects

Interpreters don’t need to know theoretical details—the interface guides the entire process.

Calibrate the model

• Fit Vp, Vs, and density trends vs. porosity and Vshale
• Use fluid zones to constrain saturation effects
• Incorporate core measurements if available
• Check crossplots and diagnostics in real time

Save as an RPT

• One template per zone or facies
• Reusable, sharable, version-controlled
• Zero coding required

RPTs are the model “brains” that will later be applied across entire reservoir models and inversion volumes.

Step 2: Estimate the Wavelet (Single or Wavelet Volume)

To generate synthetic seismic, interpreters need a wavelet that reflects the actual data:

SubsurfaceAI allows you to:

• Extract single wavelets (well ties)
• Generate wavelet volumes (AVO/angle-dependent wavelets)
• QC synthetic vs. real traces directly in the interface

Accurate wavelets translate into reliable forward seismic modeling.

This wavelet becomes a key input for Step 7.

Step 3: Import Static and Dynamic Reservoir Models Into SubsurfaceAI

Reservoir models are typically in Eclipse grid format with corner-point geometry. SubsurfaceAI supports:

• Static reservoir models
• Dynamic simulation models (pressure, saturation changes over time)
• Facies grids
• Irregular pillar grids (non-vertical pillars)

Each model becomes a native object in your SubsurfaceAI project.

This integration means interpreters no longer need:

• Third-party preprocessing tools
• Reservoir-engineering software
• Painful manual reformatting

Everything is imported with a few clicks.

Step 4: Upscale Reservoir Properties Onto a Regular Seismic Grid

Reservoir grids and seismic grids are inherently different:

• Reservoir grids → irregular, simulation-friendly
• Seismic grids → regular, interpretation-friendly

SubsurfaceAI includes an advanced Upscaling Module that:

Inputs reservoir properties:

• Porosity
• Sw/So/Sg (saturation)
• Vshale or facies
• Pressure

Outputs seismic-grid volumes:

• Regularized porosity
• Regularized Vshale
• Regularized saturation
• Regularized pressure

Upscaling options include:

• Volume-weighted averaging
• Facies-aware upscaling
• Contact-preserving interpolation
• Options to minimize boundary smearing

Upscaling is more than resampling—it’s critical for preserving reservoir integrity when moving into elastic space.

Step 5: Assign RPTs to Zones and Facies

Now tell SubsurfaceAI which RPT applies where.

Two intuitive methods:

1. Zone-based assignment

• Upper/middle/lower reservoir zones
• Flow units or sequence boundaries

2. Facies-based assignment

• Using Vshale thresholds
• Using facies codes
• Using regional geological knowledge

You can create rules such as:

• Vshale < 0.3 → clean-sand RPT
• 0.3 < Vshale < 0.5 → mixed-facies RPT
• Vshale > 0.5 → shale RPT

This ensures that each grid cell uses the correct physics.

Step 6: Compute Vp, Vs, and Density From RPTs + Reservoir Properties

This is where RPM does the heavy lifting.

For each grid cell, SubsurfaceAI:

1. Identifies the correct RPT
2. Reads the cell’s porosity, Vshale, saturation, pressure
3. Applies the model to compute:
   • Vp
   • Vs
   • Density

Outputs:

• 3D Vp volumes
• 3D Vs volumes
• 3D density volumes
• One set per simulation time step (for dynamic models)

Interpreter-friendly QC tools

• Inline/crossline display
• Histogram and trend comparisons
• Elastic-property crossplots
• Well-based validation

At this point, your static/dynamic reservoir model has been transformed into a fully elastic model, ready for synthetic seismic generation.

Step 7: Generate Synthetic Seismic Volumes (Including 4D)

Using the elastic volumes (Vp, Vs, density) and the wavelet, SubsurfaceAI generates:

Synthetic pre-stack gathers

• Angle-dependent reflectivity
• Offset-dependent amplitudes

Synthetic partial stacks

• Near
• Mid
• Far

Synthetic full-stack volumes

• Stratigraphic imaging across the field

4D synthetic seismic (monitor–base)

• For dynamic reservoir models
• Time-step-by-time-step simulation
• Monitoring pressure depletion
• Tracking waterflood
• Identifying gas breakthrough

These synthetic volumes represent exactly what the reservoir model predicts seismic should look like.

Step 8: Compare Synthetic and Real Seismic – The Key Interpretive Step

SubsurfaceAI’s comparison tools allow interpreters to evaluate:

• Amplitude match
• Tuning behavior
• Waveform similarity
• Lateral continuity
• 4D seismic changes

Visual Comparison

• Overlay or side-by-side
• Transparency blend
• Inline/crossline switching
• Time/depth slices

Quantitative Comparison

Using the Seismic Volume Calculator, interpreters can compute:

• Synthetic–real difference
• Normalized difference
• Ratio differences
• Cross-attribute comparisons
• 4D difference maps

Interpretation Becomes Diagnostic

If synthetic ≈ real:

• Model assumptions (porosity, N:G, saturation) are plausible
• Fluid movement matches observed 4D changes

If synthetic ≠ real:

• Reservoir model needs updating
• Perhaps saturation transitions differ
• Or facies distribution is wrong
• Or pressure behavior is inconsistent

This is actionable feedback for reservoir engineers and geomodelers.

Why This Workflow Elevates the Role of the Seismic Interpreter

Traditionally, interpreters were expected to deliver:

• Horizons
• Faults
• Inversion products
• AVO maps

But with SubsurfaceAI, interpreters now contribute:

• Physics-based reservoir model validation
• Elastic-to-seismic consistency checks
• 4D seismic interpretation integrated with simulation models
• Quantitative geophysical diagnostics for geomodeling updates

Interpreters become integrated partners in reservoir management, not just providers of structural surfaces.

This dramatically enhances the influence and value of geophysics within multidisciplinary teams.

Key Benefits of Using SubsurfaceAI for Reservoir Validation

For Seismic Interpreters

• Understand how reservoir changes affect seismic
• Diagnose mismatches confidently
• Eliminate guesswork in amplitude interpretation
• Directly test geological or reservoir hypotheses

For Geomodelers

• Receive physics-based seismic feedback on facies, N:G, or porosity assumptions
• Identify where the model must be revised

For Reservoir Engineers

• Improve history matching
• Validate or adjust saturation and pressure evolution
• Strengthen production forecasting

For Asset Teams

• Reduce uncertainty
• Improve development planning
• Make better drilling decisions

Looking Ahead: What Comes Next in the Series

In Part 2, we focused on the forward direction:

Reservoir model → RPT → Vp/Vs/Density → Synthetic seismic → Compare with real seismic

In Part 3, we flip the process:

Real seismic inversion → Vp/Vs/Density → RPT inverse modeling → Porosity, lithology, saturation

In Part 4, we explore how machine learning drastically simplifies rock physics tasks—clustering facies, auto-calibrating models, scaling across 3D surveys.

In Part 5, we introduce interactive What-If seismic analysis, enabling interpreters to explore how seismic responds to thickness, N:G, and fluid changes.