Reservoir Porosity and Permeability Estimation from Well Logs using Fuzzy Logic and Neural Networks

Abstract

Abstract Petroleum reservoir characterization is a process for quantitatively describing various reservoir properties in spatial variability using all the available field data. Porosity and permeability are the two fundamental reservoir properties which relate to the amount of fluid contained in a reservoir and its ability to flow. These properties have a significant impact on petroleum fields operations and reservoir management. In un-cored intervals and well of heterogeneous formation, porosity and permeability estimation from conventional well logs has a difficult and complex problem to solve by statistical methods. This paper suggests an intelligent technique using fuzzy logic and neural network to determine reservoir properties from well logs. Fuzzy curve analysis based on fuzzy logics is used for selecting the best related well logs with core porosity and permeability data. Neural network is used as a nonlinear regression method to develop transformation between the selected well logs and core measurements. The technique is demonstrated with an application to the well data in offshore Korea. The results show that the technique can make more accurate and reliable reservoir properties estimation compared with conventional computing methods. This intelligent technique can be utilized a powerful tool for reservoir characterization from well logs in oil and natural gas development projects. Introduction Reservoir characterization is a process of describing various reservoir characteristics using all the available data to provide reliable reservoir models for accurate reservoir performance prediction. Reservoir characterization plays a crucial role in modern reservoir management. The reservoir characteristics include pore and grin size distributions, permeability, porosity, facies distribution, and depositional environment. The types of data to need for describing the characteristics are core data, well logs, well tests, production data and seismic survey. Especially well log data can provide valuable but indirect information about mineralogy, texture, sedimentary structures and fluid content of a reservoir. Generally, well logs appear to be continuous information with intensive vertical resolutions. Reservoir porosity and permeability are the two fundamental rock properties which relate to the amount of fluid contained in a reservoir and its ability to flow when subjected to applied pressure gradients. These properties have a significant impact on petroleum fields operations and reservoir management. In un-cored intervals and well, the reservoir description and characterization methods utilizing well logs represent a significant technical as well as economic advantage because well logs can provide a continuous record over the entire well where coring is impossible. However, porosity and permeability estimation from conventional well logs in heterogeneous formation has a difficult and complex problem to solve by conventional statistical methods. This paper suggests an intelligent technique for reservoir characterization using fuzzy logic and neural network to determine reservoir properties from well logs. Simple cross-plotting each input against the output may give an indication of the quality of linear or multiple linear regression models that could be formed. For more complicated relationships found in many oil field problems, such simple tools often do not provide adequate solutions. Fuzzy ranking algorithm can be used to select inputs best suited for predicting the desired output. Fuzzy curve analysis based on fuzzy logics is used for selecting the best related input (well logs) with output (core porosity and permeability). Parametric methods like statistical regression require the assumption and satisfaction of multi-normal behavior and linearity. Therefore, neural network as a non-linear and non-parametric tool is becoming increasingly popular in well log analysis. Neural network is a computer model that attempts to mimic simple biological learning processes and simulate specific functions of human nervous system. Neural network can be used as a nonlinear regression method to develop transformation between the selected well logs and core analysis data.