Prediction of Stimulated Rock Volume Using Minimum-Volume Enclosing Ellipsoid Fitting Algorithm

Jun Cheng

doi:10.54097/qkghb925

Authors

Jun Cheng

DOI:

https://doi.org/10.54097/qkghb925

Keywords:

Hydraulic Fracturing; Microseismic Monitoring; Embedded Discrete Fracture Model; Critical Pore Pressure; Stimulate Rock Volumetric.

Abstract

This paper proposes an improved Embedded Discrete Fracture Model (EDFM) that integrates fluid flow-fracture mechanics mechanisms with microseismic event triggering criteria to achieve high-precision dynamic simulation of hydraulic fracture propagation and prediction of Stimulated Rock Volume (SRV). The model innovatively introduces critical pore pressure criteria and anisotropic permeability correction algorithms, addressing deficiencies in traditional PKN/KGD models that overlook matrix pore pressure and mixed fracture mechanisms. By combining Monte Carlo simulations with machine learning classification algorithms, microseismic event classification accuracy has been improved to 85%, while SR prediction errors have been reduced to within 8% compared to traditional methods. Case studies demonstrate that this model can effectively guide optimization in hydraulic fracturing design.

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