Machine Learning-Assisted Comparative Analysis of Fracture Propagation Mechanisms in CO2 and Hydraulic Fracturing of Acid-Treated Tight Sandstone

Carbon dioxide (CO2) fracturing and acid treatment are currently considered promising approaches to overcome the challenge of excessively high initiation pressure during conventional hydraulic fracturing in tight sandstone gas reservoirs. However, the mechanisms of these methods weaken the reservoir rock’s mechanical properties, remain unclear. Using a machine learning approach, we elucidate the differences in initiation mechanisms between CO2 fracturing and hydraulic fracturing under acid-treated conditions, thereby providing a mechanistic explanation for the lower initiation pressure observed in CO2 fracturing compared to conventional hydraulic fracturing. The tensile fractures, shear fractures, and acid-modified fractures have been identified by a specially trained AI model, which achieved exceptional accuracy (95.4%). Acoustic emission source locations show that CO2 fracturing mainly causes shear fracture along acid-weakened planes, which promotes the propagation of composite tensile-shear fractures in untreated reservoir areas. Due to the significantly lower diffusivity of water compared to CO2, hydraulic fracturing predominantly induces non-acidic mixed-mode (tensile-shear) fractures. This fundamental difference in fracture patterns accounts for the higher initiation pressure observed in hydraulic fracturing compared to CO2 fracturing. These findings offer crucial insights into pressurized fluid-driven fracturing mechanisms and propose an optimized technical pathway for enhancing hydrocarbon recovery in low-permeability sandstone formations.