Permeability Estimation Model from Grain Size Sieve Analysis: Data of Onshore Central Niger Delta

Abstract

Permeability is an important property of the soil and studies have shown that grain size distribution is a controlling factor to this property. Establishing an empirical equation that shows the relationship between permeability and grain size has been previously investigated by several researchers, all of whom have been able to develop models for fast permeability prediction using grain size data. But because of the complexity of permeability and the Earth’s anisotropic nature, the confidence level of using this models is low as was seen when a comparison was carried out in this project using some of these models. The aim of this project is to develop a model using grain sieve analysis data for permeability prediction tailored to the Niger Delta region. Using statistica7 software, multiple regression analysis was performed on the grain size distribution data from sieve analysis using parameters P10, P90 and mean grain size distribution. Three models were developed for permeability ranges of less than 10mD to greater than 10000mD with R2 values of (0.857, 0.820, 0.939) showing a good data and regression fitting and R values of (0.926, 0.906, 0.969) showing strong positive correlation between variables. Permeability values obtained from routine core analysis was compared to the predicted permeability gotten from the model equation produced by the regression analysis. The models displayed good correlation with the routine core analysis values as seen on the validation charts plotted. A coloured schemed 3-D surface plot was generated to display the integrated effect of the grain size and density on permeability. The sensitivity analysis carried out showed that proper grain sorting is essential in permeability prediction.