Abstract

Relative permeability is a key index in resource exploitation, energy development, environmental monitoring, and other fields. However, the current determination methods of relative permeability are inefficient and invisible without considering wetting order and pore structure characteristics either. In this study, microfluidic experiments were designed for figuring out key factors impacting on the two-phase relative permeability. The optimized intelligent image recognition was established for saturation extraction. The deep learning was conducted for the prediction of two-phase permeability based on the inputs from microfluidic experiments and image recognition and optimized. Results revealed that phase saturation, wetting order, and pore topology were the key factors influencing the two-phase relative permeability, with the importance of 38.22%, 34.84%, and 26.94%, respectively. The deep learning-based relative permeability model performed well, with MSE < 0.05 and operational efficiency of 3 ms/epoch. Aiming at relative permeability model optimization, on the one hand, the dividing ratio of training set and testing set for flooding phase relative permeability prediction achieved the highest prediction accuracy at 7 : 3, while that for displaced phase was 6 : 4. On the other hand, tanh() activation function performed 40% more accurate than the sigmoid() activation function.

Highlights

  • The relative permeability is a crucial parameter reflecting reservoir rock allocation properties and an indispensable index revealing the characteristics of fluid flow and distribution [1, 2]

  • Microfluidic experiments were designed for figuring out key factors impacting on the two-phase relative permeability

  • The optimized intelligent image recognition was established for saturation extraction

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Summary

Introduction

The relative permeability is a crucial parameter reflecting reservoir rock allocation properties and an indispensable index revealing the characteristics of fluid flow and distribution [1, 2]. Relative permeability is a key index in resource exploitation, energy development, environmental monitoring, and other fields [3, 4]. Relative permeability is a key indicator to determine the characteristics of sewage diffusion and transport. The construction of a relative permeability model with high efficiency, high accuracy, high robustness, and extensive applicable scenarios is of great significance to effectively evaluate resource mining efficiency, improve energy recovery, and optimize environmental monitoring and testing [5,6,7,8]

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