Microseismic Drainage Pattern Identification via Moment Tensor Analysis
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Solution Overview
Problem
Current methods for predicting flow patterns in stimulated reservoirs during enhanced oil recovery (EOR) in unconventional shale gas and oil projects are limited, as they neglect vertical extent and connected linear features, leading to inaccurate assessment of drainage areas and production volumes.
Innovation Solution
A method and system that utilize microseismic event data to determine moment tensor data, infer crack formation, and calculate streamlines representing predicted fluid flow, providing a graphical representation of flow patterns in the reservoir, thereby identifying drainage patterns and improving the accuracy of reservoir modeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to predict flow patterns in stimulated reservoirs, then the assessment process is simple, but the accuracy of drainage area and production volume assessment deteriorates due to neglecting vertical extent and connected linear features
Solution Approach 1:
The patent transitions from 2D planar representations to 3D spatial modeling by incorporating vertical extent (z-dimension) through moment tensor analysis. This allows the system to capture the three-dimensional geometry of fracture networks, including dip angles and vertical propagation, thereby improving drainage area assessment accuracy while managing modeling complexity through systematic data processing
2Measurement precision
If microseismic event data is analyzed using moment tensor analysis to infer crack formation, then the accuracy of flow pattern prediction improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex microseismic dataset into individual event analyses, processing each microseismic event separately through moment tensor inversion. This segmentation allows systematic extraction of crack orientation and geometry parameters from numerous small events, improving overall flow pattern prediction accuracy while managing computational complexity through modular processing of discrete events
Solution Approach 2:
The patent introduces moment tensor analysis as an intermediary step between raw microseismic data and crack formation inference. This intermediary mathematical framework transforms complex wavefield data into interpretable physical parameters (strike, dip, orientation) that directly inform flow pattern modeling, thereby improving prediction accuracy while structuring the data processing pipeline
3Measurement precision
If conventional 2D methods are used for reservoir stimulation monitoring, then the monitoring system is simpler to implement, but the assessment of stimulated reservoir volume and drainage patterns becomes inaccurate
Solution Approach 1:
The patent enhances conventional 2D monitoring by incorporating the vertical dimension through moment tensor analysis of microseismic events. This 3D approach captures fracture dip angles, vertical extent, and spatial orientation, providing accurate stimulated reservoir volume assessment while maintaining systematic data processing through automated moment tensor inversion and streamline calculation
Data Source
AI summary
A method and system for identifying reservoir drainage patterns from microseismic data for illustrating flow paths towards ports. The method includes: determining moment tensor data for each of a plurality of microseismic events in the reservoir; inferring crack formation data in the reservoir in dependence on the moment tensor data; and calculating stream lines that represent predicted flow of fluids through the reservoir in dependence on the inferred crack formation data.