SRV Overlap Detection via Microseismic 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 with 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 monitoring of SRV overlap between stages, allowing for dynamic adjustment of treatment strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If microseismic data with low signal-to-noise measurements are used to identify SRVs, then more comprehensive fracture coverage is achieved, but measurement precision and reliability of SRV estimation deteriorate
Solution Approach 1:
The patent extracts and removes outlier microseismic events and low-density events from the dataset. By filtering out these unreliable measurements, the system maintains comprehensive fracture coverage while eliminating data points that degrade measurement precision. This selective extraction allows the system to work with only the most reliable microseismic events for SRV calculation.
Solution Approach 2:
The patent changes the parameter of data quality by applying density-based filtering criteria. Events are evaluated based on their spatial density and statistical properties, and parameters such as event density thresholds and outlier detection limits are adjusted to optimize the balance between comprehensive coverage and measurement precision in SRV estimation.
2Shape
If all microseismic events including outliers are included in SRV calculation, then fracture geometry coverage is improved, but reliability of SRV identification deteriorates
Solution Approach 1:
The patent extracts outlier events from the complete microseismic dataset using statistical analysis and density-based filtering. By removing these unreliable events while retaining the majority of valid events, the system maintains comprehensive fracture geometry coverage while significantly improving the reliability of SRV identification.
Solution Approach 2:
The patent implements a feedback mechanism where the SRV calculation process continuously evaluates the quality and reliability of included microseismic events. Based on this feedback, the system adjusts the filtering criteria and iteratively refines the event selection to optimize both fracture geometry coverage and SRV identification reliability.
3Quantity of substance
If detailed analysis of low-amplitude microseismic events is performed, then fracture detection completeness is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent changes the measurement parameters by establishing amplitude thresholds and signal-to-noise ratio criteria for event inclusion. This parameter transformation allows the system to automatically filter and analyze low-amplitude events based on quantitative thresholds, reducing the subjective complexity of detecting and measuring these subtle seismic signals while maintaining detection completeness.
4Productivity
If real-time monitoring of SRV overlap is implemented, then treatment efficiency is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the SRV analysis into discrete computational stages corresponding to each hydraulic fracturing stage. By calculating SRV boundaries and overlap metrics stage-by-stage rather than analyzing the entire treatment simultaneously, the system reduces real-time computational complexity while maintaining treatment efficiency through progressive monitoring and adjustment.
Data Source
AI summary
In some aspects, a first boundary is computed based on microseismic event locations associated with a first stage of a multi-stage injection treatment of a subterranean region. A second boundary is computed based on microseismic event locations associated with a second stage of the multi-stage injection treatment. Based on the first and second boundaries, an overlap between stimulated reservoir volumes (SRVs) associated with the first and second stages is determined.