The application of committee Machine with Intelligent Systems to the prediction of permeability from petrographic image analysis and well logs data: a case study from the South Pars gas field, South Iran

Permeability is the ability of porous rock to transmit fluids. An accurate knowledge of reservoir permeability is necessary for reservoir management and development. This study presents an improved model based on the integration of petrographic data, conventional logs, and intelligent systems to predict permeability. Petrographic image analysis was employed to measure the optical porosity, pore types, pore morphologies, mineralogy, amount of cement, and type of texture. Available conventional log measurements include bulk density, neutron porosity, and natural gamma ray. The permeability was first predicted using the individual intelligent systems including a neural network (NN), a fuzzy logic (FL), and a neuro-fuzzy (NF) model. Afterwards, two types of committee machine with intelligent systems (CMIS) were used to combine the permeability values calculated from the individual intelligent systems: simple averaging and weighted averaging. In the weighted averaging, a genetic algorithm model was employed to obtain the optimal contribution of each expert. The results show that both of the CMIS performed better than NN, FL, and NF models acting alone

Improving the Accuracy of Flow Units Prediction through Two Committee Machine Models: an Example from the South Pars Gas Field, Persian Gulf Basin, Iran

Intelligent reservoir characterization is a prerequisite study for development of oil and gas fields. Hydraulic flow units are mappable portions of hydrocarbon-bearing rocks that control fluid flow, and their modeling allows an accurate understanding of reservoir quality within a hydrocarbon field. The current study presents two committee machines based on intelligent models to make a quantitative formulation between flow units and conventional log responses in the South Pars Gas Field, Iran. First, a committee machine with intelligent systems (CMIS) is constructed using a genetic algorithm-pattern search technique. The overall mean squared error and coefficient of determination between the measured and predicted flow zone indicators (FZI) using the CMIS for the test data are 0.1468 and 0.775, respectively. Afterwards, a committee fuzzy inference system (CFIS) is constructed. For this purpose, the training data are divided into two individual clusters based on the FZI values. The two FZI clusters are trained with the individual fuzzy inference systems, and a classifier network assigns appropriate weights to each cluster. The MSE and coefficient of determination of the CFIS are 0.1233 and 0.812, respectively. The CFIS shows some improvement in the accuracy of predictions in comparison with the CMIS. The results of this study demonstrate the higher performance of the committee machines compared to the individual expert systems for estimating reservoir properties. Moreover, a primary clustering of the model data and their training with the individual expert systems can lead to a considerable improvement in the accuracy of committee machines

A reservoir rock porosity estimation through image analysis and fuzzy logic techniques

Petrophysical properties of petroleum reservoir rocks are usually obtained by laborious core laboratory measurements. The present study investigates the capability of petrographic image analysis applied on thin sections of reservoir rock and fuzzy logic for predicting porosity in carbonate rocks. The proposed methodology comprises two steps: first, the petrographic parameters, including porosity type, grain size, mean geometrical shape coefficient of grains, and texture type, were extracted for each thin section based on image analysis techniques. Consequently, the petrographic parameters were formulated to core porosity using a Takagi and Sugeno fuzzy inference system. Petrographic image analysis is an emerging technology, which provides fast and accurate quantitative evaluation from reservoir rock. The results of single petrographic image analysis showed inaccurate estimation of total porosity in all rocks except those that have an extremely isotropic pore structure. A quantitative evaluation of thin section images and fuzzy model was successfully used to improve the accuracy of porosity prediction and the results of thin section analysis were generalized to core plug analysis. The mean square error and correlation coefficient between two-dimensional measurements and core plug were obtained at 0.0262 and 86.3, respectively, which shows acceptable prediction of three-dimensional porosity from two-dimensional thin sections. Therefore, the results confirmed the validity of the propounded methodology

Semi-automated porosity identification from thin section images using image analysis and intelligent discriminant classifiers

Identification of different types of porosity within a reservoir rock is a functional parameter for reservoir characterization since various pore types play different roles in fluid transport and also, the pore spaces determine the fluid storage capacity of the reservoir. The present paper introduces a model for semi-automatic identification of porosity types within thin section images. To get this goal, a pattern recognition algorithm is followed. Firstly, six geometrical shape parameters of sixteen largest pores of each image are extracted using image analysis techniques. The extracted parameters and their corresponding pore types of 294 pores are used for training two intelligent discriminant classifiers, namely linear and quadratic discriminant analysis. The trained classifiers take the geometrical features of the pores to identify the type and percentage of five types of porosity, including interparticle, intraparticle, oomoldic, biomoldic, and vuggy in each image. The accuracy of classifiers is determined from two standpoints. Firstly, the predicted and measured percentages of each type of porosity are compared with each other. The results indicate reliable performance for predicting percentage of each type of porosity. In the second step, the precisions of classifiers for categorizing the pore spaces are analyzed. The classifiers also took a high acceptance score when used for individual recognition of pore spaces. The proposed methodology is a further promising application for petroleum geologists allowing statistical study of pore types in a rapid and accurate way