Saeed Rafiee; Abdolnabi Hashemi; Mohammad Shahi
Abstract
Petrophysical parameters such as porosity, water and oil saturations, formation resistivity factor, etc. describe the storage capability of the porous media or the capacity of rocks to hold fluids. The modified Archie’s equation . / . , also called the saturation equation, is used ...
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Petrophysical parameters such as porosity, water and oil saturations, formation resistivity factor, etc. describe the storage capability of the porous media or the capacity of rocks to hold fluids. The modified Archie’s equation . / . , also called the saturation equation, is used to determine the water saturation. Archie’s parameters, namely , , and , are sometimes assumed constant to simplify petrophysical measurements. But these parameters are not constant, particularly in heterogeneous reservoirs. Inaccurate estimates of these parameters can cause significant errors in the calculation of water saturation when using Archie’s equation and lead to discrepancies between log interpretation and production test results. There are many factors affecting cementation factor () such as porosity, pore throat size, type of rock grains, type and distribution of clay content, degree of cementation, and overburden pressure. In the present paper, the results of electrical resistivity experiments are used to derive a new cementation factor correlation which can be applied to carbonate parts of Asmari and Sarvak formations located in south-west Iran. In Iran, the cementation factor is traditionally measured by Shell formula or is assumed equal to 2 to avoid difficulty. In the new formula, increases with increasing porosity; however, in the Shell formula, decreases with increasing porosity especially in the low porosity ranges, which is in disagreement with the current paper results. In addition, the results demonstrate that it is not possible to introduce constant values or separate cementation factor correlations versus porosity for different petrofacies and rock types. Petrophysical evaluations are done to quantify hydrocarbon resources in formations under study. Then, the water saturation is calculated with different calculation methods of cementation factor, . The calculated water saturations are compared with the measured water saturations of preserved cores.
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.
