Seismic Termination Detection and Classification via Edge Point Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for computer-assisted seismic stratigraphy lack the capability for automatic detection and classification of seismic terminations, which are crucial for identifying hydrocarbon accumulations in subsurface regions, as they rely on manual interpretation and do not effectively utilize data quality and clarity requirements.
Innovation Solution
A computer-implemented method that analyzes seismic data volumes to identify edge points, determine termination directions, and classify seismic terminations into types such as truncation, toplap, onlap, and downlap by using seismic attributes and geometric criteria, including the use of waveform anomalies and relative surface orientations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation methods are used to identify seismic terminations, then the interpreter can examine and mark terminations with expertise, but the process is time-consuming and lacks automation
Solution Approach 1:
The system performs automatic detection and classification of seismic terminations without requiring manual intervention. The computer-implemented method independently identifies edge points, determines termination directions, and classifies termination types (truncation, toplap, onlap, downlap) based on seismic data attributes and geometric criteria, making the system self-sufficient in the interpretation task
Solution Approach 2:
The manual mechanical process of interpreting seismic data and marking terminations is replaced by an automated computer-based system. The method uses algorithmic processing of seismic attributes and geometric relationships to detect and classify terminations, substituting human manual operations with automated computational mechanisms
2Extent of automation
If existing computer-assisted methods detect convergences using directional vectors, then automation is achieved, but the methods lack the capability to classify terminations into specific types
Solution Approach 1:
The method segments the termination detection process into distinct functional components: edge point identification, termination direction determination, and termination type classification. Each component processes specific aspects of the seismic data independently, allowing the system to maintain automation while preserving detailed classification information for different termination types
Solution Approach 2:
The system changes the analytical parameters from simple convergence detection to multi-parameter analysis including edge point coordinates, termination direction angles, and geometric relationships between adjacent surfaces. These parameter changes enable the system to not only detect terminations automatically but also classify them into specific types based on their geometric characteristics
3Shape
If flowline density methods are used to pinpoint boundaries, then continuous boundary lines can be generated, but the method requires high data quality and clarity without specifying requirements
Solution Approach 1:
The method uses edge points as intermediary elements to connect and define termination boundaries. By identifying discrete edge points and determining their spatial relationships and termination directions, the system constructs continuous boundary representations without requiring the high data quality conditions needed by flowline density methods
Data Source
Figure 1A~1D
Figure 2
Figure 3
AI summary
The present disclosure provides a system and method for automatically identifying and classifying seismic terminations within a seismic data volume. A set of surfaces is obtained (step 303) describing the seismic data volume. A plurality of seismic terminations is identified within the set of surfaces (step 307). Based upon seismic attributes or geometric criterion, a termination direction can be determined (step 309) for at least one termination.