Seismic Constrained Discrete Fracture Network Generation
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Solution Overview
Problem
Current seismic interpretation methods are inadequate in accurately characterizing subsurface structures, particularly in identifying fracture sets and their orientations, which are crucial for optimizing resource extraction and reservoir management.
Innovation Solution
A method that involves receiving seismic amplitude variation with azimuth data to compute values based on components of a second-rank tensor, selecting fracture heights and azimuths, and generating a discrete fracture network, which includes fractures of different sets, to predict permeability and optimize well placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional seismic interpretation methods are used, then the process is simple and quick, but the accuracy of subsurface structure characterization is insufficient
Solution Approach 1:
The patent segments the fracture characterization process into distinct components: generating multiple discrete fracture networks with different fracture sets, computing tensor values for each configuration, and selecting the optimal configuration based on match with observed seismic data. This segmentation allows systematic improvement of accuracy while managing complexity through modular processing steps.
Solution Approach 2:
The patent introduces azimuthal orientation as an additional dimension for characterizing fracture networks. By considering fractures with different azimuths and computing tensor values in multiple orientations, the method captures subsurface structure complexity that conventional single-dimension methods miss, thereby improving characterization accuracy.
2Reliability
If discrete fracture networks with multiple fracture sets are generated, then the accuracy of permeability prediction improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-generating multiple discrete fracture network configurations with different fracture sets and orientations before the actual permeability prediction. This allows the system to evaluate multiple scenarios in advance and select the optimal configuration, improving prediction reliability while managing computational complexity through structured pre-processing.
Solution Approach 2:
The patent systematically varies key parameters such as fracture azimuth, fracture set number, and tensor components to generate multiple fracture network configurations. By changing these parameters and evaluating their impact on permeability prediction, the method identifies optimal configurations that maximize accuracy while maintaining computational feasibility.
3Loss of information
If seismic amplitude variation with azimuth data is used for inversion, then the detail of fracture network information improves, but the data processing complexity increases
Solution Approach 1:
The patent develops a multi-functional inversion framework that simultaneously extracts multiple fracture network parameters (azimuth, tensor values, fracture set characteristics) from seismic amplitude variation with azimuth data. This universal approach consolidates multiple processing functions into a unified workflow, reducing overall processing complexity while maximizing information extraction.
Solution Approach 2:
The patent introduces tensor values as intermediary parameters that bridge seismic amplitude variation data and fracture network characteristics. By computing tensor values from seismic data and using them to constrain fracture network generation, the method simplifies the inversion process while preserving detailed fracture information that would otherwise require direct complex modeling.
Data Source
AI summary
A method can include receiving values of an inversion based at least in part on seismic amplitude variation with azimuth (AVAz) data for a region of a geologic environment; based at least in part on the received values, computing values that depend on components of a second-rank tensor aij; selecting a fracture height for fractures in the geologic environment; selecting an azimuth for a first fracture set of the fractures; based at least in part on the values for the second-rank tensor aij, the fracture height and the selected azimuth, determining an azimuth for a second fracture set of the fractures; and generating a discrete fracture network (DFN) for at least a portion of the region of the geologic environment where the discrete fracture network (DFN) includes fractures of the first fracture set and fractures of the second fracture set.