Fault Surface Extraction from Seismic Attribute Volumes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning models in hydrocarbon exploration fail to accurately determine fault planes from seismic data, leading to inadequate resolution and accuracy in identifying hydrocarbon-bearing formations and understanding subterranean structures.
Innovation Solution
The method involves determining fault attribute volumes from seismic data, where each fault sample includes inline, crossline, depth, amplitude, and vertical thickness values, with dip and azimuth values calculated using plane fit approximation, allowing for the extraction of fault surfaces and integration into geological models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to determine fault attributes from seismic data, then the exploration efficiency is improved, but the resolution and accuracy of geological interpretation of fault planes deteriorates
Solution Approach 1:
The patent segments the fault characterization process into distinct stages: initial machine learning-based fault attribute determination followed by subsequent refinement steps that enhance resolution and accuracy of fault plane interpretation, allowing both efficiency and precision goals to be met
Solution Approach 2:
The patent introduces intermediary processing steps between the machine learning model output and the final geological interpretation, including additional attribute calculations and refinement algorithms that serve as mediators to improve accuracy without sacrificing the initial efficiency gain from automated ML processing
2Extent of automation
If machine learning models are used to determine fault attributes, then automation is improved, but the accuracy of identifying hydrocarbon-bearing formations deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the initial machine learning-based fault attribute determination is refined through subsequent processing steps that use the initial results as input, iteratively improving the accuracy of hydrocarbon-bearing formation identification while maintaining the automated workflow
Solution Approach 2:
The patent performs preliminary machine learning-based fault attribute determination to establish initial fault models, which then serve as the foundation for subsequent refinement steps that improve accuracy, allowing automation to handle the preliminary work while reserved capacity is dedicated to accuracy-critical refinement phases
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of fault surface computation and structural interpretation, improving the identification of hydrocarbon-bearing formations and reducing drilling hazards by providing a more precise understanding of subterranean structures.
Implementation Method 1
A dip value and azimuth value can be determined at the location of each fault sample by applying a plane fit approximation to a group of nearby points from the neighbor traces
Data Source
AI summary
Hydrocarbon exploration and extraction can be facilitated by determining fault surfaces from fault attribute volumes. For example, a system described herein can receive a fault attribute volume for faults in a subterranean formation determined using seismic data. The fault attribute volume may include multiple traces with trace locations. The system can determine a set of fault samples for each trace location. Each fault sample can include fault attributes such as a depth value, an amplitude value, and a vertical thickness value. The system can determine additional fault attributes such as a dip value and an azimuth value for each fault sample of each trace location. The system can determine fault surfaces for the faults using the fault samples and fault attributes. The system can then output the fault surfaces for use in a hydrocarbon extraction operation.