M.A Sarparandeh; B Mehrgini; A Mollajan; F Sahabi; G.H Norouzi; G Jozanikohan
Abstract
In evaluating the quality of the reservoir sandstone facies, clay usually has significant effect in reducing reservoir effective porosity, permeability as well as calculation accuracy of formation fluids saturation. There are several methods for identifying and measuring the amount of clay. In sandstone ...
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In evaluating the quality of the reservoir sandstone facies, clay usually has significant effect in reducing reservoir effective porosity, permeability as well as calculation accuracy of formation fluids saturation. There are several methods for identifying and measuring the amount of clay. In sandstone reservoirs, diversity of type and amount of clay minerals may change the capacity of cation exchange (CEC) measured in the reservoir rocks. The last parameter (i.e. CEC) can be an important criterion for zoning of reservoir based on the type of clay minerals. Cation Exchange Capacity (CEC) measurement is used as one of the subsidiary clay typing methods. This parameter is the ability of clay to absorb and release of cations in the surrounding solution, which has a specified range for each clay mineral. In cases of clay mixtures, CEC values tend toward the range of the dominant clay type of sample. In this study, cation exchange capacity of the clay minerals has been calculated in two wells of the Gonbadli Gas Field in the Shurijeh sandstone reservoir. First, CEC of 20 samples has been measured using Bower method and employing intelligent estimator based on neural network as well. Based on the petrophysical logs and laboratory results, an appropriate model was fitted to estimate this parameter in well interval. According to the CEC values of clay minerals, existing data classified into five categories including clean zone and zones of clay containing kaolinite, chlorite-illite, halloysite with two water molecules and montmorillonite. For this purpose Bayesian, Parzn and K- nearest neighbor (KNN) classifiers were used. Finally, the obtained results in comparison with the results of X-ray diffraction experiments (XRD) showed good agreement.
H Nikoogoftar; A Bahroodi; B Tokhmchi; G.H Norouzi; B Mehrgini
Abstract
Identifying and interpreting subsurface heterogeneities, especially Litofacies, plays definitely an important role in assessing and managing hydrocarbon resources. Variety of methods have been developed in order to model discrete features of hydrocarbon reservoirs, as Litofacies, which the majority of ...
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Identifying and interpreting subsurface heterogeneities, especially Litofacies, plays definitely an important role in assessing and managing hydrocarbon resources. Variety of methods have been developed in order to model discrete features of hydrocarbon reservoirs, as Litofacies, which the majority of them have focused on intra-well modeling, and are not applicable for 2D or 3D modeling between oil wells. Furthermore, developing a novel methodology to bring a more factual reservoir facies has always been a matter of attraction, and is effective in lowering risk of decision making in different exploratory stages. These days, Markov Chains is used as a powerful tool for facies modeling. This method is based on conditional probabilistic and providing transitional matrix of states. This study is carried out on an oil field, South-West Iran; where the Asmari Formation is its main reservoir. Here, interval of the Asmari Formation and its cap rock in a 12 kilometers long section, 110 meters width, is classified into three main parts, by the means of Markov Chains modeling. The best result of modeling was obtained with nine wells and four seismic horizons that brought 87% accuracy in average.