Petroleum reservoir characterization is a process for quantitatively describing various reservoir properties in spatial variability using all the available field data. Especially in early development stages, estimation of petroleum reserves and productivity is essential for the development and management of an oil and gas field. 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(Lim and Kim, 2004). These properties have a significant impact on petroleum fields operations and reservoir management.
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.
First, principal components analysis (PCA) is used to summarize the data effectively and to reduce the dimensionality of the data without a significant loss of information. The PCA is the technique for reducing, summarizing and simplifying the multiple dimension variables and analyzing the complicated patterns among the dependent variables correlated each other. Now it is mostly used as a tool in exploratory data analysis and for making predictive models. PCA involves the calculation of the eigenvalue decomposition of a data covariance matrix or singular value decomposition of a data matrix, usually after mean centering the data for each attribute.
Second, fuzzy clustering is used to determine electrofacies. Data clustering is the process of dividing data elements into classes or clusters so that items in the same class are as similar as possible, and items in different classes are as dissimilar as possible. In hard clustering, data is divided into distinct clusters, where each data element belongs to exactly one cluster. In fuzzy clustering, data elements can belong to more than one cluster, and associated with each element is a set of membership levels. These indicate the strength of the association between that data element and a particular cluster. Fuzzy clustering is a process of assigning these membership levels, and then using them to assign data elements to one or more clusters(Cuddy, 1998).
Third, artificial neural network were used to predict reservoir properties from clustered electrofacies. Neural networks have been successfully used in a variety of related petroleum engineering applications such as reservoir characterization, optimal design of stimulation treatments, and optimization of field operations.
Result of this study can be used to predict the reservoir properties that are necessary for evaluation of formation and determination of production plan.