Tracking Geologic Objects via Transformation Vectors in Seismic Data
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Solution Overview
Problem
Current seismic data interpretation methods, both manual and automated, face challenges in accurately and efficiently tracking complex geologic objects like salt/shale diapirs, channels, and faults due to limitations in data quality and geological complexity, leading to increased time and error in interpretation.
Innovation Solution
A method using transformation vectors to identify and visualize geologic objects across multiple cross sections within a geologic data volume, allowing for the estimation and display of deformation and movement, enabling more accurate and efficient detection of geologic anomalies and boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated methods are used to track geologic objects, then interpretation time is reduced, but accuracy deteriorates in complex geological settings
Solution Approach 1:
The patent segments the geologic object tracking problem into multiple cross-sectional views (inlines and crosslines). By dividing the 3D volume into 2D slices and tracking objects across these segments, the method achieves both automated efficiency and improved accuracy in complex geological settings.
Solution Approach 2:
The patent transitions from 2D cross-sectional analysis to 3D volumetric interpretation by tracking geologic objects across multiple cross sections. This dimensional approach allows automated methods to maintain accuracy in complex settings by utilizing spatial relationships in three dimensions.
2Measurement precision
If manual interpretation is used to track complex geologic objects, then accuracy is maintained, but interpretation time increases significantly
Solution Approach 1:
The patent implements automated tracking algorithms that self-correct and self-validate across multiple cross sections. The system uses transformation vectors and consistency checks between inlines and crosslines to automatically maintain accuracy without requiring manual intervention, thus reducing interpretation time while preserving precision.
Solution Approach 2:
The patent employs feedback mechanisms where the tracking results from one cross section inform and validate the tracking in adjacent sections. Transformation vectors are computed and verified through consistency checks, creating a feedback loop that maintains accuracy automatically and reduces the need for time-consuming manual review.
3Device complexity
If automated tracking assumes consistent seismic attributes, then processing is simplified, but applicability to complex geologic objects deteriorates
Solution Approach 1:
The patent applies local quality by allowing different tracking parameters and transformation models for different regions of the seismic volume. Complex geologic objects are tracked using location-specific transformation vectors that adapt to local seismic attribute variations, enabling the system to handle diverse geological structures without overwhelming complexity.
Solution Approach 2:
The patent introduces dynamic transformation vectors that adapt to changing seismic attributes across different cross sections. Rather than assuming consistent attributes throughout, the system computes transformation vectors that dynamically adjust to local variations, enhancing applicability to complex geologic objects while managing processing complexity through efficient algorithms.
Data Source
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AI summary
A method and system are described for identifying a geologic object through cross sections of a geologic data volume. The method includes obtaining a geologic data volume having a set of cross sections. Then, two or more cross sections are selected and a transformation vector is estimated between the cross sections. Based on the transformation vector, a geologic object is identified within the geologic data volume.