SRV and Fracture Network Correlation via Microseismic Data
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Solution Overview
Problem
Current methods for fracture treatments in subterranean formations do not adequately provide comprehensive information about the correlation between stimulated reservoir volume (SRV) parameters and fracture network parameters, which are crucial for accurately predicting hydrocarbon productivity and understanding the effectiveness of hydraulic fracturing efforts.
Innovation Solution
The described techniques involve correlating SRV parameters and fracture network parameters using microseismic event data to quantify and visualize the geometric and physical properties of the stimulated rock and hydraulic fracture patterns, allowing for more comprehensive analysis and simulation models for hydrocarbon production forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional fracture treatment methods are used, then treatment simplicity is maintained, but comprehensive information about SRV-fracture network correlation is insufficient
Solution Approach 1:
The patent segments the analysis into distinct SRV parameters (volume, area, shape factors) and fracture network parameters (density, orientation, spacing), allowing systematic correlation analysis between these segmented parameter sets to recover lost information without overwhelming complexity
Solution Approach 2:
The patent implements feedback by using microseismic event data to continuously refine and update the correlation between SRV parameters and fracture network parameters, enabling iterative improvement of the understanding of treatment effectiveness and productivity relationships
2Measurement precision
If detailed microseismic event data analysis is performed, then comprehensive fracture network information is obtained, but data processing complexity increases
Solution Approach 1:
The patent introduces computational algorithms and modeling techniques as intermediaries that process raw microseismic event data and transform it into meaningful fracture network parameters, thereby achieving high measurement precision while managing data processing complexity through systematic computational methods
Solution Approach 2:
The patent creates computational models and simulations that replicate the complex fracture network behavior, allowing detailed analysis of fracture patterns and their correlation with SRV parameters without requiring direct complex physical measurements, thus achieving precision through virtual modeling
3Reliability
If comprehensive SRV and fracture network parameter analysis is implemented, then hydrocarbon productivity prediction accuracy is improved, but computational resources and time are increased
Solution Approach 1:
The patent performs preliminary analysis by establishing correlation relationships between SRV parameters and fracture network parameters during the treatment process itself, using microseismic data to pre-characterize the fracture network before production begins, thereby enabling faster and more accurate productivity predictions without extensive post-treatment analysis time
Solution Approach 2:
The patent transforms complex microseismic event data into simplified correlation parameters that maintain predictive accuracy for hydrocarbon productivity while reducing computational complexity and analysis time, using parameter transformations to balance reliability and efficiency
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 understanding of hydrocarbon productivity by providing detailed information on fracture spacing, complexity, and interaction between treatment stages, enabling better visualization and control of stimulation treatments to improve hydrocarbon extraction efficiency.
Implementation Method 1
The stresses induced by the pressures generated by the fracture treatment can generate microseismic events in the subterranean formation, and the events can be detected by sensors and collected for analysis.
Data Source
AI summary
In some aspects, a stimulated reservoir volume (SRV) parameter for a stimulation treatment applied to a subterranean region is identified. A parameter of a fracture-plane network generated by application of the stimulation treatment is identified. A correlation between the SRV parameter and the fracture-plane network parameter is identified. In some implementations, the SRV and the fracture-plane network are computed based on microseismic event data associated with the stimulation treatment.