Petroleum Engineering
Arian Ahmadi; Mohammad Abdideh
Abstract
< p>The determination of rock types for petrophysical studies has a wide range of applications. It is widely used in drilling, production, and especially in the study and characterization of reservoirs. Zoning of flow units and permeability estimation is one of the challenging tasks of reservoir ...
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< p>The determination of rock types for petrophysical studies has a wide range of applications. It is widely used in drilling, production, and especially in the study and characterization of reservoirs. Zoning of flow units and permeability estimation is one of the challenging tasks of reservoir studies, which uses the integration of data from well logs and analysis of the core. In this study, a Bayesian theory-based statistical modeling method is proposed to identify hydraulic flow units in coreless wells using the concept of hydraulic flow unit and then permeability estimation. In the flow zone indicator (FZI) method, the formation is divided into five hydraulic flow units. In the Winland R35 ethod, however, it is divided into four hydraulic flow units. The Bayesian statistical model divides the existing complex carbonate reservoir rock data into three hydraulic flow units with the most probability of similarity. The second and third hydraulic flow units have closer properties compared to the first hydraulic unit. The Bayesian method-based permeability estimation modeling has acceptable precision, and validation of its results with core data indicates a precision factor of 0.96. The findings of this study can help in better understanding of the concept of flow units and more effective estimation of the permeability of the rocks of the heterogeneous carbonate reservoir.
Hamid Heydari; Jamshid Moghadasi; Reza Motafakkerfard
Abstract
Cementation factor is a critical parameter, which affects water saturation calculation. In carbonate rocks, due to the sensitivity of this parameter to pore type, water saturation estimation has associated with high inaccuracy. Hence developing a reliable mathematical strategy to determine these properties ...
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Cementation factor is a critical parameter, which affects water saturation calculation. In carbonate rocks, due to the sensitivity of this parameter to pore type, water saturation estimation has associated with high inaccuracy. Hence developing a reliable mathematical strategy to determine these properties accurately is of crucial importance. To this end, genetic algorithm pattern search is employed to find accurate cementation factor by using formation resistivity factor and the porosity obtained from laboratory core analyses with considering the assumption that tortuosity factor is not unity. Subsequently, particle swarm optimization (PSO) fuzzy inference system (FIS) was used for the classification of cementation factor according to the predominated rock pore type by using the input variables such as cementation factor, porosity, and permeability to classify the core samples in three groups, namely fractured, interparticle, and vuggy pore system. Then, the experimental data which was collected from Sarvak formation located in one of the Iran southwestern oil fields was applied to the proposed model. Next, for each class, a cementation factor-porosity correlation was created and the results were used to calculate cementation factor and water saturation profile for the studied well. The results showed that the constructed model could predict cementation factor with high accuracy. The comparison between the model presented herein and the conventional method demonstrated that the proposed model provided a more accurate result with a mean square error (MSE) of around 0.024 and led to an R2 value of 0.603 in calculating the water saturation.
