Mono-frequency seismic horizon picking via frequency filtering
Solution Overview
Problem
Horizon picking in seismic data is challenging due to complexity and poor signal-to-noise ratio, making it time-consuming and error-prone, especially when dealing with multiple frequency components in seismic data.
Innovation Solution
The method involves filtering seismic data to mono-frequency volumes, identifying a single frequency that provides continuity along a target horizon, and using this filtered data for accurate horizon picking, which enhances the reliability and speed of the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual horizon picking is used, then accuracy can be maintained, but time consumption increases and error rate increases
Solution Approach 1:
The patent segments the seismic data into multiple frequency components and processes each frequency separately to identify horizons. By dividing the complex seismic data into manageable frequency segments, the system can automatically pick horizons more efficiently while maintaining accuracy, thus reducing time consumption without sacrificing measurement precision.
Solution Approach 2:
The patent changes the frequency parameter of the seismic data by filtering and selecting specific frequency components that enhance horizon continuity. This parameter transformation enables automatic horizon picking algorithms to operate more effectively, reducing both time consumption and error rates while maintaining high accuracy.
2Loss of time
If automatic horizon picking algorithms are used, then time consumption is reduced, but accuracy decreases due to complexity and poor signal-to-noise ratio
Solution Approach 1:
The patent extracts specific frequency components from the seismic data that are most useful for horizon identification. By taking out and isolating these critical frequency components, the system can feed cleaned, enhanced data to automatic picking algorithms, thereby maintaining high accuracy while reducing time consumption.
Solution Approach 2:
The patent introduces frequency filtering as an intermediary step between raw seismic data and horizon picking algorithms. This intermediary processing cleans the data by removing noise and enhancing signal continuity, enabling automatic algorithms to achieve both speed and accuracy.
3Quantity of substance
If multi-frequency seismic data is used, then comprehensive geological information is captured, but signal-to-noise ratio deteriorates and horizon continuity is reduced
Solution Approach 1:
The patent segments the seismic data by frequency and analyzes each segment separately. This segmentation allows the system to identify which frequency components provide the best signal-to-noise ratio and horizon continuity, filtering out noisy components while preserving useful geological information.
Solution Approach 2:
The patent applies different frequency filters to different regions or aspects of the seismic data, selecting local quality characteristics (frequency components) that maximize signal-to-noise ratio and horizon continuity in each specific geological context, thereby maintaining reliability while preserving comprehensive information.
4Reliability
If frequency filtering is applied to enhance horizon continuity, then signal-to-noise ratio improves, but data complexity increases due to multiple processing steps
Solution Approach 1:
The patent performs frequency filtering and data preparation as preliminary actions before horizon picking. By pre-processing the seismic data to enhance signal-to-noise ratio and continuity, the system simplifies subsequent horizon picking operations, reducing overall processing complexity despite the additional filtering step.
Solution Approach 2:
The patent dynamically selects frequency components and adjusts filtering parameters based on the specific geological context and data characteristics. This dynamic approach allows the system to adapt processing complexity to actual needs, applying only the necessary filtering steps for each specific horizon picking task.
Data Source
AI summary
Seismic data obtained from a seismic survey conducted of a subterranean region is received. The seismic data includes multiple frequency components, having a frequency bandwidth. A target, to-be-picked horizon is identified by displaying well data on the seismic section and correlating the seismic reflector with layer tops in the well data. A horizon represents a seismic reflector between two geological layers in the subterranean region. A single frequency that gives rise to a predetermined continuity along the target horizon is determined from the frequency bandwidth of the seismic data. The seismic data is filtered to mono-frequency volumes. The mono-frequency seismic volumes include a single frequency component. A horizon is picked corresponding to the target horizon based on the mono-frequency volumes. The identified horizon corresponding to the target horizon is output for determining geological features of the subterranean region based on the identified horizon.