Abstract
Studies of rock stress sensitivity are mainly focused on experimental and data processing methods, and the mechanism cannot be adequately explained using specific pore shape models. This study, based on a random pore network simulation, explains the rock stress sensitivity mechanism for the first time. Based on the network model theory, the hydraulic conductivity equation, the dimensionless radius equation, and the effective stress equation for partially saturated rock are used to generate a three-dimensional random pore network model based on the QT platform. The simulation results show that the influence of the effective stress on the dimensionless radius becomes more significant as the aspect ratio decreases, and the relationship between dimensionless radius and effective stress can be effectively interpreted through different combinations of pore shapes. Moreover, the mechanism behind permeability stress sensitivity can be explained by establishing the relationship between permeability and effective stress.
Highlights
Rock stress sensitivity refers to the changes in rock petrophysical parameters caused by effective stress, including porosity stress sensitivity [1,2,3], permeability stress sensitivity [4, 5], and stress sensitivity of electrical resistivity [1, 6], among which permeability stress sensitivity is most frequently discussed [4, 5]
Use of the pore space-based imaging network model is relatively computationally expensive [12], whereas regular pore network models set the pore size and distribution and adopt certain distribution functions by integrating percolation theory, which results in the pore network model featuring the same complexity as that of a real rock [13]
The permeability stress sensitivity mechanism is explained by establishing the relationship between permeability and the dimensionless radius function
Summary
Rock stress sensitivity refers to the changes in rock petrophysical parameters caused by effective stress, including porosity stress sensitivity [1,2,3], permeability stress sensitivity [4, 5], and stress sensitivity of electrical resistivity [1, 6], among which permeability stress sensitivity is most frequently discussed [4, 5]. Pore network models that consider various pore shapes are preferable to study the mechanisms applicable to the micro-pore structure [11]. Pore network models include pore space-based imaging network models [12] and regular pore network models [11]. Use of the pore space-based imaging network model is relatively computationally expensive [12], whereas regular pore network models set the pore size and distribution and adopt certain distribution functions by integrating percolation theory, which results in the pore network model featuring the same complexity as that of a real rock [13]. This study, based on a QT platform, focuses on the dimensionless radius-randomized stress change by adopting a C++ program to generate randomized pore network models and integrating the different pore types, proportions, and micro-pore networks. The permeability stress sensitivity mechanism is explained by establishing the relationship between permeability and the dimensionless radius function
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