Identifying Flow Units Using an Artificial Neural Network Approach Optimized by the Imperialist Competitive Algorithm

Abstract

The spatial distribution of petrophysical propertie s within the reservoirs is one of the most importan t factors in reservoir characterization. Flow units a re the continuous body over a specific reservoir volume within which the geological and petrophysical properties are the same. Accordingly, an accurate prediction of flow units is a major task t o achieve a reliable petrophysical description of a reservoir. The aim of this paper was core flow uni t determination by using a new intelligent method. Flow units were determined and clustered at specifi c depths of reservoir by using a combination of artificial neural network (ANN) and a metaheuristic optimization algorithm method. At first, artificia l neural network (ANN) was used to determine flow units from well log data. Then, imperialist competitive algorithm (ICA) was employed to obtain the optimal contribution of ANN for a better flow unit prediction and clustering. Available rout ine core and well log data from a well in one of th e Iranian oil fields were used for this determination . The data preprocessing was applied for data normalization and data filtering before these appro aches. The results showed that imperialist competitive algorithm (ICA), as a useful optimizati on method for reservoir characterization, had a better performance in flow zone index (FZI) cluster ing compared with the conventional K-means clustering method. The results also showed that ICA optimized the artificial neural network (ANN) and improved the disadvantages of gradient-based back propagation algorithm for a better flow unit determination.