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Nitrogen gas drive and foam drive are commonly adopted for practical production in fractured carbonate reservoirs. A systematic study on nitrogen gas drive and foam assisted nitrogen gas drive in varying-aperture fractures is a practical and essential method to understand the EOR mechanism. In this study, we designed and fabricated similarity models and microscopic visual models. The fluid flow mechanisms were discussed combining the pressure response and dynamic displacement phenomenon. Experimental results show that the effective time of nitrogen gas drive was longer in decreasing-aperture fractures than that in increasing-aperture fractures. After water flooding, 0.8 PV nitrogen gas injection lead to gas channeling in decreasing-aperture fracture of 1 mm–0.2 mm while 0.6 PV nitrogen gas injection by reverse displacement direction. Both production rate and average pressure appeared discrete in narrow fracture compared to wide fracture. Enhanced oil recovery efficiency by foam drive was average 5% higher than that by nitrogen gas drive in single fracture. The displacement front of foam varies in four forms: micro-scale foam accumulation, small-scale foam accumulation, mesoscale foam slight deformation and large-scale foam serious deformation. This study could provide a theoretical basis for enhance oil recovery and further dig the potential of remaining oil in fractured carbonate reservoirs. • Designed and Fabricated similarity models and microscopic visual models of varying-aperture fractures of carbonate reservoirs. • There is a synergistic effect of gas and water flooding in fractures, which promote to oil production by regulating the position of oil-water interface in fractures. • The displacement front of foam varies in four forms: micro-scale foam accumulation, small-scale foam accumulation, mesoscale foam slight deformation and large-scale foam serious deformation. [ABSTRACT FROM AUTHOR]

The gas/water interfacial tension (IFT) is an important property that influences many aspects within the petroleum industry, e.g., the vertical distribution of the hydrocarbons and multiphase flow calculations. Laboratory measurement of IFT usually requires an expensive experimental apparatus and a sophisticated interpretation procedure. This paper presents the use of the artificial neural network (ANN) to estimate the IFT in gas/water systems. A total of 956 sets of experimental data consisting of pure methane and synthetic natural gas were acquired from previous literature reports to develop the model. Seven factors were selected as independent variables to estimate IFT using multivariate parametric regression (MPR): temperature, pressure, mole fractions of the gas compositions (CO 2 , nitrogen, methane, and ethane), and salt (NaCl) concentration in water. A three-layered (7-19-1) ANN trained with the Levenberg–Marquardt back propagation algorithm was used. The mean absolute error, mean percentage error, root mean squared error, and determination coefficient for all of the datasets were calculated to be 0.81 mN/m, 1.97%, 1.25 mN/m and 0.992, respectively, demonstrating the high estimation accuracy and strong generalization capability of the model. The performance of the ANN was further compared with a newly proposed MPR model and three explicit empirical correlations found in previous literature reports. The comparison result suggests that the estimation accuracy can be improved significantly by using ANN compared with these four other correlations. [ABSTRACT FROM AUTHOR]