Capillary pressure curves is of great importance in reservoir characterization. Due to the reservoir heterogeneity, no single capillary pressure curve can be used for the entire reservoir depth. This paper aims to examine the application of different techniques of hydraulic flow unit (HFU) classifications in overcoming the extreme heterogeneity of the reservoir in order to improve normalization of capillary pressure in one of the Iranian carbonate oil reservoirs. In this study, well log, routine and specific core analysis lab data have been used to identify flow units by employing different techniques in a heavy oil carbonate reservoir in the south of Iran. These techniques include gamma ray and density log method, flow zone indicator, capillary pressure curves and Winland parameter. Then, mercury injection data has been used by employing Desouky's method to normalize capillary pressure curves for each identified flow unit. According to this study HFU classification significantly improved normalizing of capillary pressure curves.

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This paper shows that geostatistical modeling integrated with Artificial Neural Network (ANN) can be effectively used to estimate permeability when limited amount of core and log data is available. The goal of this study is to create a good calibrated data set, and then designing a proper network for permeability estimation in one of Iranian oil reservoir. In this reservoir coring have been done only in one well. Therefore due to widely scattered and limited number of data, conventional methods of permeability estimation such as empirical equations and multi linear regressions gave poor prediction. New approach was developed in which, geostatistical modeling is performed to create comprehensive data set. After that this data set will be used to design and train neural network for prediction of permeability. In this work, at first the reservoir has been classified in to two different flow units to overcome the extreme heterogeneity of the reservoir. Then Sequential Gaussian Simulation (SGS) was performed to obtain very fine permeability distribution versus depth and log data in cored intervals of each flow unit of the reservoir. After that uncertainty analysis has been done to create data set by selecting the best points with highest probability and lowest value of standard deviation. These data were input to design and train the Co-Active Neuro Fuzzy Inference System (CANFIS) Network. This network is based on Fuzzy Logic algorithm that its parameters obtained from learning rules of neural network. Then core data are used to test the network. The results show the acceptable estimation of permeability. Finally network can effectively be used to estimate permeability in uncored but logged wells. Introduction The degree of success of many oil and gas reservoir management depends on the accuracy of the models used in a reservoir description. Permeability is an important parameter in a heterogeneous reservoir characterization. It controls the strategies involving well location, number of wells, completion and reservoir management. Permeability, therefore, is a key parameter in any reservoir characterization study that governs in great extension its handling and development. Permeability is usually measured in laboratory on core samples. However, most drilled wells are not cored. As a result, models are needed to estimate permeability in un-cored but logged wells. Estimation of permeability in un-cored but logged well is a generic problem common to all reservoirs. Many researchers applied rules of thumb developed over the years for given fields and formations to estimate rock permeability using log data in un-cored well. These rules basically stated that a relationship between porosity and permeability might be established. Petroleum engineering concepts also inspired many empirical models to estimate formation permeability from well log responses. These models established the existence of a relationship between permeability, porosity and fluid saturation. This normally requires a calibration data set that is represented by one or more key wells where comprehensive information is available in terms of core and log data. This data which is not always available in real cases, are used to build the predictor and to test the reliability of the results. Preparing good data set is one the fundamental challenges of reservoir engineers ever since the beginning of oil industry.

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