Research on the Application of Machine Learning Based Approaches for Forecasting Oil-Water Relative Permeability Curve
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-1964-2_603
Reference22 articles.
1. Yakubu, B., Draco, I., Nadimul, F., Babs, O., Gbenga, O., Ruissein, M.: Experimental investigation of the effect of temperature on two-phase oil-water relative permeability. J. Petrol. Sci. Eng. 203, 108645 (2021)
2. Sina, F., Alireza, G., Mobeen, F.: Assessment of two-phase relative permeability hysteresis models for oil/water, gas/water and gas/oil systems in mixed-wet porous media. Fuel 309, 122150 (2022)
3. Sang, Q., Zhao, X., Liu, Y., Li, Z., Dong, M.: Effects of the laminated-structure and mixed wettability on the oil/water elative permeabilities and oil productions in shale oil formations. J. Petrol. Sci. Eng. 208, 109457 (2022)
4. Zhang, L., Tong, J., Xiong, Y., Zhao, Y.: Effect of temperature on the oil–water relative permeability for sandstone reservoirs. Int. J. Heat Mass Transf. 105, 535–548 (2017)
5. Behnam, S., Fariborz, R., Tayfun, B.: Temperature effects on the heavy oil/water relative permeabilities of carbonate rocks. J. Petrol. Sci. Eng. 59, 27–42 (2007)
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