Microseismic Data Processing Using Travel-Time Adjustments
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Solution Overview
Problem
Current microseismic monitoring techniques face challenges in accurately determining source locations for underground microseismic events due to limited acoustic energy output from perforation shots, imprecise source-time information, and increased noise levels, which complicates the construction of accurate models describing elastic wave propagation.
Innovation Solution
The method involves combining subsets of microseismic data from multiple events with known source locations to enhance the signal-to-noise ratio, using travel-time adjustments and models that account for spatial relationships to improve event localization, allowing for the construction of composite microseismic data that can inform about elastic wave propagation properties without requiring detailed knowledge of the underground region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microseismic data from multiple events are combined to improve signal-to-noise ratio, then measurement precision improves, but device complexity increases due to the need for travel-time adjustments and model construction
Solution Approach 1:
The patent combines microseismic data from multiple events with known source locations to create composite data sets. By merging multiple weak signals, the method achieves a higher signal-to-noise ratio that enables more accurate determination of source locations for unknown events, while the combination process itself is simplified through automated travel-time adjustment algorithms
Solution Approach 2:
The method performs preliminary travel-time adjustments and model construction using events with known source locations before analyzing unknown events. This preliminary characterization of elastic wave propagation properties prepares the data processing framework in advance, reducing the complexity of subsequent source location determinations
2Manufacturing precision
If travel-time adjustments are applied to account for spatial relationships, then manufacturing precision improves in terms of model accuracy, but loss of time increases due to additional processing steps
Solution Approach 1:
The patent systematically adjusts travel-time parameters based on spatial relationships between source and receiver locations. By changing and optimizing these temporal parameters to account for geometric factors, the method achieves more accurate elastic wave propagation models while the automated parameter adjustment reduces manual intervention time
3Measurement precision
If data from weak microseismic events are combined, then measurement precision improves, but object-generated harmful factors increase due to accumulation of noise and uncertainties
Solution Approach 1:
The patent converts the harmful effect of individual weak signals and their associated noise into a benefit by combining multiple such signals. The random noise components tend to cancel out when multiple data sets are combined, while the coherent signal components reinforce each other, resulting in a composite data set with improved signal-to-noise ratio and reduced overall uncertainty
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 enhances the accuracy of microseismic event localization and model construction, improving the signal-to-noise ratio and enabling more reliable monitoring of hydraulic fracturing operations by combining data from weak events and accounting for localized variations, thereby optimizing fracture monitoring and reservoir parameter estimation.
Implementation Method 1
a model that describes one or more properties relating to (e.g. influencing) the propagation of elastic waves from at least one source location (e.g. from each of a plurality of potential source locations) to the plurality of receivers
Data Source
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AI summary
A method of processing microseismic data obtained by a plurality of receivers. The method includes identifying a plurality of subsets of the microseismic data, wherein each subset of the microseismic data corresponds, respectively, to a microseismic event occurring underground at a known source location. The method also includes, for each combination of source location and receiver: determining a travel-time adjustment based on a spatial relationship between the source location and the receiver;and applying the travel-time adjustment to a waveform in a subset of the microseismic data that corresponds to the combination of source location and receiver. The method also includes combining the adjusted subsets o fmicroseismic data to form composite microseismic data. The composite microseismic data may have a higher signal to noise ratio compared with the individual subsets ofmicroseismic data, and as such, may be useful in determining a model that describes one or more properties relating to the propagation of elastic waves from at least one source location to a plurality of receivers, e.g. a static model or total travel-time model.