Low-Frequency Seismic Reconstruction for Noise-Free Formation Imaging
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Solution Overview
Problem
Conventional methods struggle to effectively separate and reconstruct low frequency noise from seismic data, leading to reduced image resolution and inaccurate velocity models in seismic imaging, which is crucial for identifying hydrocarbon reservoirs.
Innovation Solution
A process and system for reconstructing low frequency signal from higher frequency signal in seismic data by transforming it to intermediate domains like frequency-slowness, frequency-velocity, or frequency-wavenumber domains, using autoregressive models to extrapolate low frequency samples, and performing reconstruction in local space-time windows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic data processing methods are used, then the processing is simpler and faster, but the image resolution and velocity model accuracy are reduced
Solution Approach 1:
The patent segments the seismic data processing into distinct frequency components (low frequency and high frequency) and processes them separately using different methods. Low frequency data undergoes complex reconstruction using autoregressive models and multiple domain transformations, while high frequency data is processed more directly. This segmentation allows each component to be optimized independently, achieving high resolution without uniformly complicating the entire processing workflow.
Solution Approach 2:
The patent performs preliminary reconstruction of low frequency seismic data before combining it with high frequency data. By pre-processing the low frequency component to enhance its quality and resolve ambiguities in advance, the subsequent combination and imaging steps become more efficient and accurate, avoiding the need for more complex iterative processing of the complete dataset.
2Reliability
If low frequency noise is not separated, then the processing is simpler, but the velocity model becomes inaccurate
Solution Approach 1:
The patent extracts and isolates the low frequency noise component from the seismic data using autoregressive modeling. By identifying and separating this specific noise component through statistical modeling and domain transformations, the method removes the harmful low frequency noise while preserving the useful low frequency signal, thereby improving velocity model accuracy without requiring overly complex noise filtering procedures.
Solution Approach 2:
The patent introduces intermediate domains (frequency-wavenumber domain, frequency-slowness domain) as mediators in the noise separation process. These intermediate domains facilitate the transformation and filtering operations by providing a representation where low frequency noise can be more easily identified and separated from the signal, making the noise removal process more effective and controlled.
3Measurement precision
If low frequency signal is not reconstructed, then the processing is simpler, but the signal-to-noise ratio decreases
Solution Approach 1:
The patent performs preliminary reconstruction of the low frequency signal component before final image formation. By using autoregressive models to predict and reconstruct missing or degraded low frequency signal portions in advance, the method enhances the signal-to-noise ratio of the low frequency component, which then serves as a improved input for subsequent imaging operations.
Solution Approach 2:
The patent replaces direct measurement and simple filtering methods with sophisticated signal processing techniques including autoregressive modeling and multi-domain transformations. This substitution of mechanical/direct methods with computational/models-based approaches enables effective reconstruction of low frequency signal, significantly improving signal-to-noise ratio despite the increased processing complexity.
Data Source
AI summary
This disclosure presents processes and systems for generating an image of a subterranean formation from seismic data recorded in a seismic survey of the subterranean formation. The seismic data is contaminated with low frequency noise in a low frequency band. Processes and systems reconstruct seismic data in the low frequency band of the seismic data to obtain low frequency reconstructed seismic data that is free of the low frequency noise. The low frequency reconstructed seismic data is used to construct a velocity model of the subterranean formation. The velocity model and the low frequency reconstructed seismic data are used to generate an image of the subterranean formation that reveals structures of the subterranean formation without contamination from the low frequency noise.