Seismic Horizon Mapping via Multi-Scale Optimization
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Solution Overview
Problem
The manual extraction of seismic horizons from large three-dimensional datasets in complex geological environments is time-consuming and costly, and existing methods are inefficient for automatic extraction of multiple horizons, particularly in reservoir characterization and stratigraphic studies.
Innovation Solution
A multi-scale optimization method using global sparse grids and constrained linear optimization, which separates the seismic horizon interpretation into pre-processing, global optimization on a sparse grid, and local optimization to achieve accurate and efficient horizon extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction of seismic horizons is performed, then accuracy of horizon identification is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs self-service by automatically extracting multiple seismic horizons through constraint-based optimization without requiring manual intervention for each horizon. The algorithm autonomously identifies horizons by optimizing constraints across the seismic volume, eliminating the need for repeated manual extraction while maintaining accuracy.
Solution Approach 2:
The system changes parameters by using optimization variables to represent horizon depths at different grid points. By adjusting these depth parameters through constrained optimization, the system automatically adapts to identify multiple horizons with varying characteristics, replacing manual parameter specification with automated parameter optimization.
2Productivity
If existing automatic extraction methods are used, then time consumption is reduced, but accuracy deteriorates in complex geological environments
Solution Approach 1:
The system implements feedback by iteratively optimizing horizon depths based on constraint satisfaction. The optimization process continuously adjusts horizon positions to meet geological constraints and seismic data consistency requirements, providing self-correction that improves accuracy in complex environments while maintaining automated efficiency.
Solution Approach 2:
The system achieves universality by using a single constraint-based optimization framework that can extract multiple different types of horizons (reflectors, interfaces, boundaries) with varying characteristics. This multi-functional approach maintains high accuracy across diverse geological scenarios without requiring separate specialized methods for each horizon type.
3Device complexity
If traditional optimization methods are used, then implementation is simple, but computational intensity and time increase for large datasets
Solution Approach 1:
The system applies segmentation by dividing the seismic volume into a sparse grid of discrete points for optimization. This segmentation reduces the computational domain from continuous to discrete, significantly lowering computational intensity while maintaining implementation simplicity through structured grid-based processing of large datasets.
Solution Approach 2:
The system uses copying by creating a simplified sparse grid representation of the continuous seismic volume. This copied discrete model serves as an optimized surrogate for processing, reducing computational intensity while preserving the essential geological features needed for accurate horizon extraction in large datasets.
Data Source
AI summary
A least one seismic attribute is determined for each voxel of the seismic volume. A first horizon is selected for mapping and a sparse global grid is generated which includes the horizon, at least one constraint point identifying the horizon, and a number of points having a depth in the seismic volume. A value of at least one seismic attribute is determined for each point and their depths are adjusted based on the value of the seismic attribute. A map of the horizon can be generated based on the adjusted depths. Multiple local grids can be generated based on the sparse global grid, and the depths of the local grid points adjusted to generate a map of the horizon at voxel level resolution. The seismic volume can be mapped into multiple horizons, where previously mapped horizons can function as constraints on the sparse global grid.