Characterization of Flow Parameters in Shale Nano-Porous Media Using Pore Network Model: A Field Example from Shale Oil Reservoir in Songliao Basin, China

Abstract

The pore-throat radius of the shale oil reservoir is extremely small, and it is difficult to accurately obtain the absolute permeability and oil–water two-phase relative permeability of the actual oil reservoir through conventional core experiments. However, these parameters are very important for reservoir numerical simulation. In this paper, a method for characterizing flow parameters based on a pore network model that considers differential pressure flow and diffusion flow is proposed. Firstly, a digital core was reconstructed using focused ion beam scanning electron microscopy (FIB-SEM) from the Gulong shale reservoir in the Songliao Basin, China, and a pore network model was extracted. Secondly, quasi-static single-phase flow and two-phase flow equations considering diffusion were established in the pore network model. Finally, pore-throat parameters, absolute permeability, and oil–water two-phase permeability curves were calculated, respectively. The results show that the pore-throat distribution of the Gulong shale reservoir is mainly concentrated in the nanometer scale; the mean pore radius is 87 nm, the mean throat radius is 41 nm, and the mean coordination number is 3.97. The calculated permeability considering diffusion is 0.000124 mD, which is approximately twice the permeability calculated without considering diffusion. The irreducible water saturation of the Gulong shale reservoir is approximately 0.4, and the residual oil saturation is approximately 0.35. The method proposed in this paper can provide an important approach for characterizing the flow parameters of similar shale oil reservoirs.

Research Status, Existing Problems, and the Prospect of New Methods of Determining the Lower Limit of the Physical Properties of Tight Sandstone Reservoirs

“…The macroscopic seepage capacity of the reservoir is strongly influenced by the microscopic pore structure and the throat of tight sandstone reservoirs. The pore size determines the ability of the rock to store oil and gas, and the radius and shape of the throat determine the seepage capacity of oil and gas [43][44][45]. For a given pressure difference, the smaller the throat radius, the higher the capillary pressure, and the greater the resistance to the flow of oil and gas, and the flow rate of oil and gas will be smaller.…”

“…Advantages: reflecting the effects of microscopic pore-throat radii on the physical properties of reservoirs based on MICP tests and yielding relatively objective PPLLs; Disadvantages: insufficient data on MICP samples and high requirements for the representativeness of the samples [43][44][45].…”

Simulations of CO2 Dissolution in Porous Media Using the Volume-of-Fluid Method

“…The Schmidt number is a dimensionless quantity that compares the momentum diffusivity to the mass By using Equation (31), the mass transfer model for our simulations can be expressed as follows: Sh = 0.004Re 0.67 Sc 0.577 (31) The precision of the model is depicted in Figure 13 through a comparison of the Sherwood number obtained from the simulation results on the x-axis with the predicted Sherwood number on the y-axis. As indicated by the black line, this model can also reasonably predict the Sherwood number.…”

“…Accurate study requires methods that capture these domains [28,29]. In pore-scale modeling, the interfacial mass transfer can effectively be considered in the context of a network of pores and throats, within which the fluid interfaces are captured semi-analytically [7,[30][31][32]. Such pore network calculations are mainly targeted towards obtaining effective properties for the mesoscale, which can further be upscaled to macroscopic transport properties at the sample level, incorporating effects of pore micro-structure and fluid micro-distribution such as thin films on transport phenomena.…”

“…Figure14compares our mass transfer model with those previously reported in the literature. Equation(31) was developed for Reynolds numbers between 0.1 and 4 and Schmidt numbers ranging from 0.089 to 8900. In Figure14, we have presented our model for Schmidt number 2000 as a solid black line.…”