Kriging with External Drift for Subsurface Velocity Mapping
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Solution Overview
Problem
Existing seismic velocity models lose resolution and accuracy away from control points due to seismic wave dissipation, making it difficult to accurately map subsurface formations and identify hydrocarbon traps.
Innovation Solution
The method employs Kriging with external drift (KED) interpolation, combining seismic data with depth to basement models generated from potential fields data to improve interpolation accuracy and reduce the need for well penetrations and surface seismic locations, thereby enhancing the resolution and accuracy of subsurface mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If seismic data is captured at control points to generate average velocity models, then the models have high resolution and accuracy near the control points, but the resolution and accuracy are lost away from the control points
Solution Approach 1:
The patent introduces potential fields data (gravity and/or magnetic) as an intermediary to bridge the gap between control points. This external drift data serves as a mediator that provides continuous spatial information across the entire region of interest, allowing the interpolation to maintain accuracy away from control points by relying on the correlated potential fields measurements that cover the entire area
Solution Approach 2:
The patent changes the parameter being interpolated from pure seismic velocity data to a combination of seismic velocity controls and potential fields data. By incorporating the external drift parameter (potential fields measurements) into the Kriging interpolation, the system transforms a localized interpolation problem into a region-wide accurate model
2Measurement precision
If more control points are added to improve velocity model accuracy across the region, then the resolution improves, but the cost of well penetrations and surface seismic locations increases
Solution Approach 1:
The patent uses potential fields data as an intermediary that provides continuous spatial coverage without requiring additional expensive control points. This mediator allows the system to achieve region-wide accuracy using the existing sparse control points combined with the densely sampled potential fields data
Solution Approach 2:
The patent effectively creates a continuous velocity model by copying the spatial information patterns from the potential fields data into the seismic velocity model. The potential fields data serves as a template that guides the interpolation, allowing the velocity model to be accurately constructed without requiring proportional increases in control point density
Data Source
AI summary
In certain embodiments, a method includes generating a set of average velocity controls based on received seismic data, generating a depth to basement model based on received potential fields data, and generating an average velocity model using an interpolation model to interpolate the set of average velocity controls and the depth to basement model. The method may also include generating a variogram model based on the set of average velocity controls, and generating the average velocity model using the interpolation model to interpolate the average velocity controls, the variogram model, and the depth to basement model. The interpolation model may be Kriging with external drift. The external drift may be based on the depth to basement model. Additionally, the method includes generating a structural map of a subsurface formation using the average velocity model.