A Comparison of Permeability Prediction Methods Using Core Analysis Data

Abstract

Permeability is one of the most important parameters of reservoir rocks, it illustrates the capacity of rock to transmit fluids (oil, gas, water) in pore spaces. Permeability data can be obtained from routine core analysis in laboratory on 1.5 in plugs and sidewall core. However, coring is limited due to cost issue, so permeability prediction in uncored sections play a significantly important role. The variety of methods developed to estimate permeability using pore scale such as Kozeny-Carman, Swanson and Pittman. In fact, those equations are applied individually to estimate permeability. In this research, permeability estimation methods will be used on the same rock (sandstone or carbonate rock) to detect the suitable method for each rock type. Porosity, permeability data from routine core analysis and pore throat size from mercury injection capillary pressure on sandstone and carbonate rocks from Cuu Long and Song Hong basins in Vietnam will be gathered and permeability estimation conducted by using Hydraulic Flow Unit (HFU), Mercury Injection Capillary Pressure (MICP) and Pittman methods based on that data. Estimated permeability obtained from each methods will be compared with core permeability, the method with the highest R-squared be selected. The research shows that Hydraulic Flow Unit is the most suitable methods for permeability prediction on sandstone with R-squared > 0.9. On the other hand, mercury injection capillary pressure is the most accurate method to estimate permeability on carbonate rocks related to heterogeneity and complicated pore system. That results will help engineers have a fast and accurate decision for permeability prediction methods selection on sandstone and carbonate rocks. In addition, the empirical equations were derived to predict permeability on sandstone and carbonate rocks with the highest coefficient of correlation in multiple regression analysis and based on the relationship between porosity, permeability and pore throat size.

Permeability Prediction of Carbonate Rocks Based on Digital Image Analysis and Rock Typing Using Random Forest Algorithm

“…Pore size is a key parameter to determine the fluid flow capacity in porous media. According to Kolodzie, Rezaee et al, and Ngo et al., there is a power-law relationship between permeability, porosity, and the representative pore-throat radius of carbonate rock. Generally, the power-law permeability model can be equivalent to the following form where r denotes the representative pore-throat radius, μm.…”

“…Winland found that r35 provided the best correlation with permeability and porosity. Afterward, several different pore-throat radii were used to predict the permeability of carbonate rocks, such as r50 and r20 . FZI is a parameter depending on the shape factor (F s ), tortuosity (τ), and specific surface area.…”

“…Afterward, several different pore-throat radii were used to predict the permeability of carbonate rocks, such as r50 21 and r20. 22 FZI is a parameter depending on the shape factor (F s ), tortuosity (τ), and specific surface area. It is often used to identify hydraulic flow units.…”

Development of HFU-based permeability prediction models using core data for characterisation of a heterogeneous Oligocene sand in the Nam Con Son basin

“…For instance, one rock containing the majority of the macro pore throat size suggests it may be a highly permeable rock and vice versa. Based on MICP data, pore throat size can be calculated directly from Washburn equation as below [4]:…”

An analytical model for real gas flow in shale nanopores with non‐circular cross‐section

“…For testing the present model, the equivalent pore radius and the geometry correction factor must be determined. We used the pore size at 20% cumulative pore volume to represent the porous media . Based on the Eq.…”