SRV Calculation Using Microseismic Outlier Filtering
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Solution Overview
Problem
Current methods for identifying and analyzing stimulated reservoir volumes (SRVs) in subterranean formations during stimulation treatments face challenges due to low-amplitude microseismic events and low signal-to-noise measurements, leading to uncertainty in fracture geometry and treatment efficiency.
Innovation Solution
The use of microseismic data to calculate and visualize SRVs through geometrical representations, such as 3D convex hulls and 2D convex polygons, which include filtering out outliers and low-density events to improve accuracy, and real-time analysis to optimize treatment strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If microseismic data with low signal-to-noise measurements is used to calculate SRV, then the treatment can capture more fracture events, but the measurement precision and reliability of SRV calculation deteriorates
Solution Approach 1:
The patent extracts and removes outlier microseismic events from the dataset that do not conform to the expected fracture geometry patterns. By identifying and eliminating these outlier points through statistical analysis and geometric consistency checks, the system retains the benefit of capturing low-amplitude events while removing the noisy measurements that degrade precision.
Solution Approach 2:
The patent applies different quality standards to different regions of the microseismic data. Events within the convex hull boundary are treated with higher confidence, while events near or outside the boundary undergo more stringent validation. This local differentiation allows the system to maintain measurement precision for reliable events while still capturing the full extent of the stimulated volume.
2Volume of moving object
If all microseismic events including outliers are included in SRV calculation, then the stimulated volume coverage is maximized, but the manufacturing precision of the SRV boundary deteriorates
Solution Approach 1:
The patent performs preliminary filtering of microseismic events before final SRV calculation by pre-identifying outliers through multiple validation criteria. This preliminary action removes problematic data points before they can corrupt the boundary definition, ensuring both comprehensive coverage and precise boundary manufacturing.
Solution Approach 2:
The system implements feedback loops where the calculated convex hull boundary is used to validate individual event positions, and events that fall outside expected geometric patterns are flagged for removal. This iterative feedback process continuously refines the boundary precision while maintaining accurate volume representation.
3Productivity
If real-time analysis of microseismic data is performed, then treatment efficiency is improved, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the real-time analysis into distinct modular components: event detection, outlier identification, convex hull calculation, and boundary validation. Each module operates independently with well-defined inputs and outputs, reducing overall system complexity while enabling real-time processing through parallel computation of these segmented functions.
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 a reliable and direct tool for field engineers to visualize and manage SRVs, enhancing the efficiency of hydraulic fracturing treatments by reducing uncertainty and improving hydrocarbon productivity.
Implementation Method 1
The pressures generated by the stimulation treatment can induce low-amplitude or low-energy seismic events in the subterranean formation, and the events can be detected by sensors and collected for analysis.
Data Source
AI summary
In some aspects, first and second boundaries are computed based on locations of microseismic events associated with a stimulation treatment of a subterranean region. Based on the first and second boundaries, an uncertainty associated with a stimulated reservoir volume (SRV) for the stimulation treatment is identified. The first and second boundaries are defined in a common spatial domain and at least a portion of the second boundary resides outside the first boundary.