Seyyed Hossein Hosseini Bidgoli; Ghasem Zargar; Mohammad Ali Riahi
Abstract
The spatial distribution of petrophysical properties within the reservoirs is one of the most important factors in reservoir characterization. Flow units are the continuous body over a specific reservoir volume within which the geological and petrophysical properties are the same. Accordingly, an accurate ...
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The spatial distribution of petrophysical properties within the reservoirs is one of the most important factors in reservoir characterization. Flow units are 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 to achieve a reliable petrophysical description of a reservoir. The aim of this paper was core flow unit determination by using a new intelligent method. Flow units were determined and clustered at specific depths of reservoir by using a combination of artificial neural network (ANN) and a metaheuristic optimization algorithm method. At first, artificial 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 routine core and well log data from a well in one of the Iranian oil fields were used for this determination. The data preprocessing was applied for data normalization and data filtering before these approaches. The results showed that imperialist competitive algorithm (ICA), as a useful optimization method for reservoir characterization, had a better performance in flow zone index (FZI) clustering 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.
Sajjad Gholinezhad; Mohsen Masihi
Abstract
The prediction of porosity is achieved by using available core and log data; however, the estimation of permeability is limited to the scare core data. Hence, porosity and saturation data through the framework of flow units can be used to make an estimation of reservoir permeability. The purpose of this ...
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The prediction of porosity is achieved by using available core and log data; however, the estimation of permeability is limited to the scare core data. Hence, porosity and saturation data through the framework of flow units can be used to make an estimation of reservoir permeability. The purpose of this study is to predict the permeability of a carbonate gas reservoir by using physical-based empirical dependence on porosity and other reservoir rock properties. It is emphasized that this new relationship has a theoretical background and is based on molecular theories. It is found out that if rock samples with different types are separated properly and samples with similar fluid-flow properties are classified in the same group, then this leads to finding an appropriate permeability/porosity relationship. In particular, the concept of hydraulic flow units (HFU) is used to characterize different rock types. This leads to a new physical-based permeability/porosity relationship that has two regression constants which are determined from the HFU method. These coefficients, which are obtained for several rock types in this study, may not be applicable to other carbonate rocks; but, by using the general form of the model presented here, based on the HFU method, one may obtain the value of these coefficients for any carbonate rock types. Finally, we used the data of cored wells for the validation of the permeability results.
