Microseismic Event Classification for Integrity Monitoring
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Solution Overview
Problem
Current microseismic monitoring systems generate vast amounts of data that require extensive manual review to identify significant incidents affecting oil and gas production operations, leading to a need for more efficient analysis and classification methods to quickly detect and address potential integrity issues.
Innovation Solution
A computer-implemented method for monitoring subsurface operation integrity using seismic data, which involves detecting microseismic waves, processing data to calculate trigger values and event attributes, and classifying events into categories based on predetermined criteria, including magnitude, proximity, and spectral density, to automate the identification of events like casing failures and other integrity threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review methods are used to analyze microseismic data, then measurement precision can be maintained, but productivity is significantly reduced due to the vast amounts of data requiring extensive manual analysis
Solution Approach 1:
The patent replaces manual mechanical review processes with automated electronic classification systems that use spectral density analysis and event attribute calculations to automatically categorize microseismic events, thereby maintaining detection accuracy while dramatically increasing data analysis productivity
Solution Approach 2:
The patent transforms the analysis approach by changing from direct manual inspection to automated parameter-based classification using spectral density ratios and event attributes, enabling rapid processing of vast microseismic datasets while preserving detection precision through systematic parameter evaluation
2Productivity
If automated classification methods are implemented, then productivity increases through rapid data processing, but device complexity increases due to sophisticated algorithms and computing requirements
Solution Approach 1:
The patent segments the complex classification task into distinct sequential steps: trigger value calculation, spectral density computation, event attribute extraction, and category assignment. This segmentation simplifies the overall system complexity while maintaining high productivity through modular, specialized processing components
Solution Approach 2:
The patent manages complexity by transforming raw microseismic data into standardized event attributes and classification categories through systematic parameter transformations, creating a structured framework that simplifies automated decision-making while enabling rapid event classification
3Measurement precision
If comprehensive event attributes are calculated, then measurement precision improves for event characterization, but loss of time increases due to extensive processing requirements
Solution Approach 1:
The patent performs preliminary calculations of spectral density and event attributes during the initial data processing phase, preparing classification-ready parameters in advance. This preliminary action enables rapid event characterization without time loss during actual classification, as all necessary parameters are pre-computed and ready for immediate use
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 method significantly reduces the time required to analyze microseismic data, enabling early detection of operational integrity issues and facilitating timely corrective actions, thereby enhancing the reliability and efficiency of oil and gas production operations.
Implementation Method 1
detecting microseismic waves in a subsurface area of operation using a seismic monitoring system
Data Source
AI summary
Methods and systems are disclosed for monitoring operation integrity during hydrocarbon production or fluid injection operations. According to the methods and systems, received microseismic data is processed to obtain a plurality of data panels corresponding to microseismic data measured over a predetermined time interval. For each data panel, trigger values are calculated for data traces corresponding to sensor receivers of the microseismic monitoring system. At least one data panel is selected as a triggered data panel that satisfies predetermined triggering criteria. At least one triggered data panel is selected as a non-trivial data panel that satisfies spectral density criteria. A value is calculated for each of at least two event attributes of a plurality of event attributes of the event. An event score is determined based on the values of the plurality of event attributes. An event is classified into at least one event category of a plurality of event categories based on the event score.