Abstract: A dynamic productivity design method for gas wells based on equivalent seepage theory and multiphase flow dynamic coupling is proposed to address the challenge of insufficient accuracy in forecasting well deliverability caused by dynamic gas saturation evolution during the capacity expansion and production ramp-up of oil reservoir-based gas storage facilities. Unlike traditional gas storage models relying on well test data, this approach establishes a conversion model between absolute gas permeability under low confining pressure in surface cores and effective oil permeability under irreducible water conditions at formation pressure. Combined with gas-oil relative permeability curves, it creates a dynamic mapping relationship between gas saturation and effective gas permeability. Using nodal analysis, the method quantifies the impact of gas saturation in the secondary gas cap zone on gas flow capacity during injection-production cycles, innovatively deriving a binomial dynamic deliverability equation. Research demonstrates that this technique overcomes the dependence on production data in existing methods, achieving effective conversion from surface permeability parameters to in-situ multiphase flow conditions, and reveals the nonlinear influence of gas saturation variation on well productivity during storage operations. Compared with conventional steady-state methods, dynamic predictions show well productivity fluctuations reaching 1.5~4.0 times steady-state values, verifying the necessity of dynamic modeling. This research provides crucial theoretical support for well type selection, pattern optimization, and dynamic parameter adjustment during cyclic operations in oil reservoir-based gas storage facilities, filling the technical gap in dynamic productivity prediction while significantly enhancing peak-shaving capacity and project economics.

Key words: oil reservoir-based gas storage, productivity prediction, gas saturation, equivalent seepage, nodal analysis