A comparative study of empirical, statistical and virtual analysis for the estimation of pore network permeability

Permeability of any porous medium is a key parameter to analyze the flow behavior and characterization of reservoir for the optimization of hydrocarbon production. Permeability is usually determined experimentally, and if no laboratory data is available, empirical correlations can be used to estimate it. In recent years, artificial neural network (ANN) modeling, have gained popularity in solving complex problems, such as prediction of permeability in heterogeneous formation. Degree of uncertainty associated with each technique requires more careful use of any other technique. The present study aims to estimate formation permeability by using three techniques, “Empirical Relations”, “Multivariate Regression Analysis” and “Virtual Measurements” that show potentials in achieving our goal. Core permeability is used as target data to test the validity of these techniques. For the purposes of this study, six wells from a heterogeneous Lower Goru formation from Sawan Gas Field, Pakistan are selected. Well log data and corresponding permeability values for these wells were available. The result shows that Morris and Biggs empirical relations provide acceptable results with measured permeability in different geological conditions of the wells. Five well log responses (gamma ray (GR), bulk density (RHOB), sonic log (DT), deep resistivity log (LLD) and neutron porosity (NPHI)), are used as inputs in the ANN to predict permeability in all wells. To ensure that the characteristic of neural network technique is not an isolated incident the same exercise is repeated in all available wells to predict permeability. Multivariate regression analysis is performed on the basis of well log response in wells to access definition of permeability in terms of wireline logs. Hybrid approach is developed in this paper by the integration of multivariate regression analysis and estimated permeability from neural network, which suggest a verifiable and accurate prediction of permeability from well logs. The results indicate that permeability can be estimated in precise and accurate manner by the integration of statistical and virtual techniques depending upon the geological conditions of the studied rock interval.