Improved ST-FZI method for permeability estimation to include the impact of porosity type and lithology

Abstract

Abstract Permeability represents the flow conductivity of a porous media. Since permeability is one of the most vital as well as the complex properties of a hydrocarbon reservoir, it is necessary to measure/estimate accurately, rapidly and inexpensively. Routine methods of permeability calculation are through core analysis and well tests, but due to problems and weaknesses of the aforementioned methods such as excessive costs and time, these are not necessarily applied on neither in all wells of a field nor in all reservoir intervals. Therefore, log-based approaches have been recently developed. The goal of this research is to provide a flowchart to estimate permeability using well logs in one of Iranian south oil fields and finally to introduce a new algorithm to estimate the permeability more accurately. Permeability is firstly estimated using artificial neural network (ANN) employing routine well logs and core data. Subsequently, it is estimated using Stoneley-Flow Zone Index (ST-FZI) and is compared with the results of core analysis. Correlation coefficients in permeability estimation by artificial neural network and Stoneley-FZI are R 2 = 0.75 and R 2 = 0.85, respectively. On the next step, an improved algorithm for permeability prediction (improved ST-FZI) is presented that includes the impact of lithology and porosity type. To improve the permeability estimation by ST-FZI method, electro-facies clustering based on MRGC method is employed. For this purpose, rock pore typing utilizing VDL and NDS synthetic logs is employed that considers the porosity types and texture. The VDL log separates interparticle porosity from moldic and intra-fossil porosities and washes out and weak rock-type zones. Employing MRGC method, three main facies are considered: good-quality reservoir rock, medium-quality reservoir rock and bad-quality (non-reservoir) rocks. Permeability is then estimated for each group employing ST-FZI method. The estimated permeability log by improved ST-FZI method shows better match with the measured permeability (R 2 = 0.93). The average error between estimated and measured permeability for ANN, ST-FZI method and improved ST-FZI method is 1.83, 1.18 and 0.796, respectively. The increased correlation is mainly due to involving the impact of porosity types on improved ST-FZI method. Therefore, it is recommended to apply this algorithm on variety of complicated reservoir to analyze its accuracy on different environments.

Keywords

• Permeability estimation
• Artificial neural network (ANN)
• Stoneley-FZI
• Rock pore typing