Data-Driven Microseismic Filtering for Enhanced Oil Recovery and CO2 Storage
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Solution Overview
Problem
Existing methods struggle to effectively recover microseismic signals from noisy observations in non-stationary environments, particularly in microquake data, due to the challenge of distinguishing signal from noise without prior knowledge of noise statistics, leading to low Signal-to-Noise Ratio (SNR) and false event detection.
Innovation Solution
A data-driven linear filtering method that estimates observation and noise correlation matrices from seismic data segments, iteratively applying a linear filter to enhance microseismic signal recovery without assuming noise type, suitable for both single-trace and array processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional filtering methods are used to remove noise from microseismic signals, then noise reduction is achieved, but signal attenuation occurs and false event detection increases
Solution Approach 1:
The patent transforms the filtering problem from the time domain to the frequency domain by applying Short-Time Fourier Transform (STFT), converting the signal representation parameters. This allows frequency-based separation of signal and noise components, enabling noise removal while preserving signal integrity and avoiding false detections that occur with time-domain filtering methods.
Solution Approach 2:
The patent extends the analysis from one-dimensional time-domain signals to two-dimensional time-frequency representations using STFT. By adding the frequency dimension, the method can selectively filter noise components at specific frequencies while preserving the temporal structure of microseismic events, thereby reducing false detections.
2Measurement precision
If noise filtering is applied to enhance signal quality, then Signal-to-Noise Ratio improves, but signal attenuation occurs
Solution Approach 1:
The patent extracts only the noise components from the time-frequency representation of the signal. By identifying and removing solely the noise portions in the frequency domain while leaving the signal components intact, the method improves SNR without causing signal attenuation that would occur with conventional filtering approaches.
3Device complexity
If simple filtering methods are used for noise removal, then processing complexity is reduced, but measurement precision of microseismic events deteriorates
Solution Approach 1:
The patent divides the continuous signal into multiple short-time segments using STFT, analyzing each segment independently in the frequency domain. This segmentation approach enables precise localization and removal of noise components associated with each time segment, improving microseismic event detection precision while maintaining computational efficiency through localized processing.
Data Source
AI summary
A data-driven linear filtering method to recover microseismic signals from noisy data/observations based on statistics of background noise and observation, which are directly extracted from recorded data without prior statistical knowledge of the microseismic source signal. The method does not depend on any specific underlying noise statistics and works for any type of noise, e.g., uncorrelated (random/white Gaussian), temporally correlated and spatially correlated noises. The method is suitable for microquake data sets that are recorded in contrastive noise environments. The method is demonstrated with both field and synthetic data sets and shows a robust performance.