Author Description

This volume compiles advanced research on unconventional reservoir engineering, addressing hydraulic fracturing, CO2 utilization, and data-driven modeling. Integrating experiments, simulations, and machine learning (ML), it offers solutions to optimize hydrocarbon recovery and sustainability. Key themes include the following: (1) fracture mechanics, exploring fracture propagation in inter-salt shales, viscoplastic fracture behavior, and ML-guided fracture spacing; (2) CO2 applications, examining its dual role in EOR and storage via injection strategies, ML-predicted flooding outcomes, and CO2-water-rock interactions affecting reservoir integrity; (3) modeling innovations, such as VOF-based pore-scale multiphase flow simulations, ML-driven fracture optimization, and productivity forecasting in condensate-rich shales; and (4) practical advances, including nanoparticle-surfactant EOR systems, high-permeability streak detection in carbonates, coal creep analysis, and gas storage well-testing protocols. Bridging nanoscale fluid dynamics to field-scale CO2 management, this book equips researchers and engineers with tools to enhance recovery efficiency, mitigate environmental impacts, and leverage emerging technologies for the sustainable development of unconventional resources in a low-carbon era.