A Study on the Effective Tracking of Hydraulic Fracturing Fracture Development Based on Microseismic Data

The study of hydraulic fracturing fracture development paths based on microseismic data is of great significance and value for obtaining fracture development morphology, studying fracture characteristics, and evaluating the production of tight oil and gas reservoirs. However, existing methods for simulating fracture networks based on microseismic data mainly rely on spatiotemporal clustering analysis of seismic sources, which lack theoretical support and cannot provide path information for seismic sources. To address this issue, this study proposes an innovative method for modeling fracture development paths. The method first conducts clustering analysis based on the P-axis direction of seismic sources, dividing seismic data into different clusters, and performing regional stress inversion to determine the principal stress direction of each cluster. This direction is assumed to be the direction of fracture development, and the main fracture path is obtained based on this direction. Subsequently, the secondary fracture paths are constructed based on seismic parameters for the remaining seismic points. The method has been validated with multiple sets of measured microseismic data. The results show that it has high accuracy and reliability in predicting the direction of fracture development, guiding and optimizing fracturing construction plans, and evaluating predicted oil and gas production capacity. It provides a new method and perspective for understanding the development of hydraulic fracturing reservoirs and assessing the effectiveness of oil and gas production capacity. Highlights The proposal of a new tracking method for hydraulic fracture development paths based on microseismic monitoring data. Enhanced accuracy in predicting fracture paths through the integration of seismic source mechanism analysis and stress inversion. Strengthened capability to characterize fracture network features and assess fracturing effects using clustering techniques.