Frequency-Domain Waveform Fitting for Microseismic Event Location
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Solution Overview
Problem
Current microseismic data analysis techniques require significant human intervention for time picking and are subjective, time-consuming, and prone to errors, especially when dealing with noisy data, and they do not effectively utilize the full receiver array for noise rejection, which hampers accurate location of microseismic events during hydraulic fracturing operations.
Innovation Solution
A waveform fitting approach is employed in the frequency domain for microseismic data processing, which includes least-squares time-reversal and waveform fitting methods to analyze three-component microseismic data, eliminating the need for time picking and utilizing the complete polarization vector, allowing for automated and objective analysis with quantified uncertainties, and enabling the estimation of source and model parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time-picking methods are used for microseismic event location, then human intervention can identify seismic arrivals, but the process becomes subjective, time-consuming, and prone to errors especially with noisy data
Solution Approach 1:
The system performs automated picking of seismic arrivals using algorithms that independently identify P-wave and S-wave onsets without human intervention. The method uses waveform characteristics and arrival time patterns to automatically determine event locations, making the system self-sufficient and eliminating subjective human judgment while maintaining accuracy.
Solution Approach 2:
The patent replaces manual time-picking (mechanical human operation) with automated computational methods. The system uses signal processing algorithms, correlation techniques, and waveform analysis to automatically identify seismic arrivals, substituting human mechanical picking with automated electronic processing that is faster and more consistent.
2Loss of information
If manual time picking is performed for each seismic detector, then discrete arrival times can be obtained, but the process requires considerable human intervention and quality control
Solution Approach 1:
The patent combines data from multiple seismic detectors into a unified analysis framework. Instead of picking arrivals separately for each detector, the system simultaneously processes waveforms from all detectors to identify seismic events and their arrivals, merging individual detector information into a coherent joint solution that reduces operational complexity.
Solution Approach 2:
The automated picking system serves multiple functions simultaneously: it picks arrivals for P-waves, picks arrivals for S-waves, identifies event locations, and performs quality control all through a single integrated process, eliminating the need for separate manual operations for each function.
3Productivity
If automated picking algorithms are used to handle large volumes of microseismic data, then processing speed increases, but the algorithms often get misled by noisy and complicated data
Solution Approach 1:
The system incorporates feedback mechanisms where the automated picking algorithm continuously refines its estimates by comparing predicted arrivals with actual observed waveforms. The method uses iterative optimization where picking results feed back into the model to improve subsequent picks, allowing the system to distinguish true seismic signals from noise through repeated refinement.
Solution Approach 2:
The patent employs parameter changes in the form of adjusting picking thresholds, waveform windowing parameters, and signal-to-noise ratio criteria dynamically based on data characteristics. The system adapts its processing parameters to the specific noise conditions of each dataset, improving reliability by changing operational parameters rather than using fixed thresholds.
4Measurement precision
If current mapping methods are used to locate seismic events, then three-dimensional event locations can be determined, but the methods are subjective and time-consuming requiring human intervention
Solution Approach 1:
The system performs preliminary actions by pre-calculating travel time tables and ray paths through the subsurface before actual event location is needed. These pre-computed models are stored and reused for multiple events, eliminating the need to repeatedly perform complex forward modeling for each event location, thus reducing processing complexity while maintaining three-dimensional accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides improved accuracy and efficiency in locating microseismic events by automating the data analysis process, reducing human intervention, and enhancing signal-to-noise ratio, while allowing for the determination of source and model parameters with quantified uncertainties, thereby improving the monitoring of microseismicity in subterranean formations.
Implementation Method 1
A waveform fitting approach is employed in the frequency domain for microseismic data processing
Implementation Method 2
least-squares time-reversal and waveform fitting methods to analyze three-component microseismic data
Implementation Method 3
utilizing the complete polarization vector, allowing for automated and objective analysis with quantified uncertainties
Data Source
AI summary
Methods and systems for processing microseismic waveforms. The methods and systems provide determining a measure of waveform fit in the frequency-domain comprising constructing, in the frequency-domain, at least one of an amplitude misfit functional and a cross phase functional between arrivals; and estimating source parameters and/or model parameters.