Seismic Data Noise Suppression via Spatial Window Randomization
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Solution Overview
Problem
Seismic data analysis in the oil and gas industry is hindered by noise, particularly internal multiples that resemble primary seismic events, making it difficult to obtain reliable subsurface images due to near-surface geological complexity and imperfect data quality.
Innovation Solution
A method involving data processing that includes flattening seismic data based on identified events, dividing it into spatial windows, randomizing, filtering to suppress noise, and reorganizing to maintain coherent primary events, effectively separating noise from primary reflections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional filtering methods are used to remove noise from seismic data, then noise suppression is achieved, but primary seismic events are also attenuated along with the noise
Solution Approach 1:
The seismic data is divided into multiple spatial windows along the trace direction. Each window is processed independently through randomization and filtering operations. This segmentation allows the filter to target noise in specific local regions without affecting primary events in other regions, thereby suppressing noise while preserving primary seismic events.
Solution Approach 2:
The patent applies randomization to transform the ordered seismic data into a disordered state, then applies filtering, and finally reverses the randomization process to restore the original order. This inversion approach allows the filter to operate on randomized data where noise and primary events are mixed, then recover only the primary events in their original positions after de-randomization, achieving noise suppression without losing primary event integrity.
2Manufacturing precision
If aggressive noise filtering is applied to improve data quality, then noise is reduced, but the complexity of the processing increases
Solution Approach 1:
By dividing the data into spatial windows and processing each window independently with standardized randomization and filtering operations, the method achieves high data quality through localized processing. This segmentation approach manages complexity by breaking down a complex global filtering problem into simpler, repeatable local operations that can be efficiently implemented.
3Object-affected harmful factors
If randomization is applied to seismic data for noise suppression, then coherent noise is reduced, but the processing time increases
Solution Approach 1:
The randomization and filtering process is applied to segmented spatial windows rather than the entire dataset at once. This allows parallel processing of multiple windows, reducing overall processing time while maintaining the noise suppression benefits of randomization. Each window is processed independently and efficiently, then reassembled to form the final processed dataset.
Data Source
AI summary
The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for suppressing noises in seismic data. One computer-implemented method includes receiving, at a data processing apparatus, a set of seismic data associated with a subsurface region; flattening, by the data processing apparatus, the set of seismic data according to an identified seismic event; dividing, by the data processing apparatus, the set of seismic data into a plurality of spatial windows; randomizing, by the data processing apparatus, the set of seismic data according to a random sequential order; filtering, by the data processing apparatus, the randomized seismic data; and reorganizing, by the data processing apparatus, the filtered seismic data according to a pre-randomization order.