TIME TO DEPTH
Seismic time-to-depth conversion is essential for accurately positioning seismic reflections in the subsurface. There are several methods, depending on the available velocity information and the complexity of the geology.
TIME TO DEPTH conversion methods
Vertical Time-Depth Conversion (Layer Cake Model)
Vertical Time-Depth Conversion (Layer Cake Model)
Vertical Time-Depth Conversion (Layer Cake Model)
Uses a simple layer-based approach where velocity is assumed constant or linearly varying within layers. This method is used for Layered subsurface with minimal lateral variations.
- Steps:
- Define velocity functions for different layers.
- Convert two-way travel time (TWT) of each layer to depth using interval velocity of layers.
- Integrate the layer thickness to obtain final depth model.
Interval Velocity Model & Dix Equation
Vertical Time-Depth Conversion (Layer Cake Model)
Vertical Time-Depth Conversion (Layer Cake Model)
Converts stacking velocity (obtained from seismic processing) to interval velocity using Dix's equation. This method is used for simple geological structures with small velocity variations.
- Steps:
- Obtain RMS velocities from velocity analysis.
- Compute interval velocities using Dix’s equation.
- Integrate interval velocities to obtain depth.
Well-Tied Velocity Model
Vertical Time-Depth Conversion (Layer Cake Model)
Ray-Tracing Depth Conversion
Uses well velocity data (check-shot, sonic logs) to calibrate the seismic velocity model. This method is used for areas with well control data available.
- Steps:
- Obtain well velocity profiles and seismic stacking velocity.
- Adjust the seismic velocity model to match well depths.
- Perform depth conversion using updated velocity functions.
Ray-Tracing Depth Conversion
Artificial Intelligence (AI) & Machine Learning-Based Method
Ray-Tracing Depth Conversion
Uses ray tracing through a velocity model to map seismic reflections from time to depth. This method is used for highly complex geological settings with strong lateral velocity variations.
- Steps:
- Construct a velocity model with depth-dependent and lateral variations.
- Use ray-tracing algorithms to convert reflection times to depths.
Geostatistical Methods (Kriging, Bayesian Inversion)
Artificial Intelligence (AI) & Machine Learning-Based Method
Artificial Intelligence (AI) & Machine Learning-Based Method
Uses geostatistics to integrate various velocity sources and uncertainty modeling. This method is used for uncertainty analysis and integration of multiple velocity sources.
- Steps:
- Combine well velocities, seismic stacking velocities, and geological information.
- Use kriging or Bayesian inversion to generate the most probable velocity field.
- Perform time-to-depth conversion.
Artificial Intelligence (AI) & Machine Learning-Based Method
Artificial Intelligence (AI) & Machine Learning-Based Method
Artificial Intelligence (AI) & Machine Learning-Based Method
Uses AI models trained on historical well and seismic data to predict velocity models. This method is used for data-rich environments with complex geological settings.
- Steps:
- Train AI models using existing well and seismic data.
- Predict interval velocities for new locations.
- Convert seismic time data to depth.
Stacking velocity model building with stacking vertical functions with ESSO format(CDP, Time, Velocity Col.)
VELOCITY MODEL BUILDING methods
Dix Velocity Method
The Dix velocity model is a method used in seismic processing to estimate interval velocities from root-mean-square (RMS) velocities. It is based on Dix's equation, which assumes a horizontally layered subsurface.
Constrained Velocity Inversion Method
The Constrained Velocity Inversion (CVI) method is used to convert stacking velocity in the time domain into interval velocity in the depth domain while honoring geological and geophysical constraints. This is crucial for depth imaging and quantitative seismic interpretation.
Advantages of Constrained Velocity Inversion:
✅ Provides geologically reasonable velocity models.
✅ Avoids errors from direct Dix inversion.
✅ Uses constraints from well logs and geological knowledge.
✅ Essential for accurate depth imaging in complex structures.
Steps of Constrained Velocity Inversion
1. Input Data:
- Stacking velocity from velocity analysis.
- RMS velocity calculated from stacking velocity.
- Initial time-depth relationship from well logs or Dix inversion.
- Geological constraints such as stratigraphic boundaries.
2. Convert Stacking Velocity to RMS Velocity.
3. Dix Equation to Compute Initial Interval Velocity.
Using the Dix equation, compute the interval velocity in the
time domain. This gives an initial estimate of the
interval velocity.
4. Time-to-Depth Conversion:
Convert two-way travel time to depth using the interval
velocity model. This builds an initial depth model.
5. Apply Constraints:
- Well-log velocity calibration.
- Structural smoothing to prevent unrealistic variations.
- Depth trend fitting using geological models.
6. Iterative Inversion Process:
- Compare derived depth with well tops.
- Apply corrections using iterative least-squares fitting.
- Ensure velocity gradients match geological expectations.
7. Output:
- Final interval velocity model in depth that is geophysically consistent.
- Used for depth migration and seismic interpretation.
Seismic velocity model building is the process of constructing a velocity field that accurately represents subsurface properties. This model is crucial for seismic imaging, depth migration, and time-to-depth conversion.
Geological model and sonic well log are the main constraints use to control the Dix velocity model.
Comparison of Dix and CVI interval velocity model. The right panel is the difference of both velocity models. The main difference is due to effect of well and geology boundaries.
Comparison of time and depth sections using CVI velocity model.
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