Iterative Horizon Picking in 3D Seismic Data
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Solution Overview
Problem
Current 3D seismic data interpretation methods lack an efficient and automated process for horizon picking and attribute representation, which hinders the accurate visualization and understanding of subsurface geology in petroleum exploration.
Innovation Solution
A system and method for iterative horizon picking in 3D seismic data, starting from seed points, where each point is processed multiple times to identify neighboring points, recording iteration numbers and attributes like the number of descendants and propagation direction, and displaying these attributes visually to enhance geological insight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated horizon picking algorithms are applied to 3D seismic data, then productivity is improved, but measurement precision of geological features deteriorates due to lack of detailed attribute information
Solution Approach 1:
The system records iteration numbers and propagates attribute information through multiple iterations of the picking algorithm, allowing the algorithm to refine its results by feedback from previously identified features and their attributes
Solution Approach 2:
The system changes parameters by recording multiple attributes (iteration numbers, descendant counts, propagation directions) for each picked point, transforming the output from simple coordinate lists to richly annotated geological features with multiple measurable properties
2Measurement precision
If detailed attribute information is recorded for each picked point, then measurement precision is improved, but device complexity increases due to additional data storage and processing requirements
Solution Approach 1:
The system segments the attribute information into distinct categories (iteration numbers, descendant counts, propagation directions) that can be independently processed and stored, making the complex data structure more manageable
3Manufacturing precision
If multiple iterations of the picking algorithm are performed, then manufacturing precision is improved, but loss of time increases due to repeated processing
Solution Approach 1:
The system performs preliminary actions by recording iteration numbers and attribute information during the picking process itself, rather than requiring separate post-processing steps, thus eliminating wasted time
Data Source
AI summary
A system and method may, based on a 3D seismic data set seed point, execute a seed picking algorithm, using the first point for picking a set of second points from the data set, setting each of the points in the set of second points as the first point and repeating the algorithm. An iteration number or other attribute may be assigned to the points, the iteration number corresponding to the number of times the algorithm repeated to process the point. The attribute or a number of attributes may be displayed as a visual characteristic for each point. An iterative process may be applied to a set of seismic data points, starting at a seed data point and finding a set of next iteration seed points from among the set of points neighboring the seed point, continuing only with next iteration seed points, and recording for each of a set of data points the number of points that are found by the process when the point is used as a seed data point. Attributes may include, for example, the total number of descendants of a seed point, the direction, for example, the azimuth, of propagation of the horizon picking process, or information that relates to the order in which points are picked such as an iteration number.