Mahdi Rastegarnia; Ali Kadkhodaie
Abstract
Flow unit characterization plays an important role in heterogeneity analysis and reservoir simulation studies. Usually, a correct description of the lateral variations of reservoir is associated with uncertainties. From this point of view, the well data alone does not cover reservoir properties. Because ...
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Flow unit characterization plays an important role in heterogeneity analysis and reservoir simulation studies. Usually, a correct description of the lateral variations of reservoir is associated with uncertainties. From this point of view, the well data alone does not cover reservoir properties. Because of large well distances, it is difficult to build the model of a heterogenic reservoir, but 3D seismic data provides regular sampling that can improve reservoir spatial description. In this study, seismic attribute analysis was used to predict flow zone indicator (FZI) values of a carbonate reservoir by using seismic and well log data. First, a 3D acoustic impedance volume was created as an external attribute for seismic data analysis. To improve the ability of FZI prediction, the maximum number of attributes from multiattribute analysis was computed by using a step-wise regression technique. To verify the results of multiattribute technique, the cross plot analysis of multiattribute method was performed. It was found that the R2 value of the correlation between the predicted and actual FZI is as high as 0.859 with an average error value of 2.34 µm. The analysis of the results of multiattribute technique showed that it was an effective technique for FZI prediction in hydrocarbon reservoirs. Such accuracy in building a 3D distribution of FZI provides a good insight into reservoir production zones. The results clearly indicate that the methodology proposed herein can successfully be used to specify the locations of new wells for the purpose of future production or injection plans.
Mohsen Karimian; Nader Fathianpour; Jamshid Moghaddasi
Abstract
Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with ...
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Porosity is considered as an important petrophysical parameter in characterizing reservoirs, calculating in-situ oil reserves, and production evaluation. Nowadays, using intelligent techniques has become a popular method for porosity estimation. Support vector machine (SVM) a new intelligent method with a great generalization potential of modeling non-linear relationships has been introduced for both regression (support vector regression (SVR)) and classification (support vector classification (SVC)) problems. In the current study, to estimate the porosity of a carbonate reservoir in one of Iran south oil fields from well log data, the SVR model is firstly constructed; then the performance achieved is compared to that of an artificial neural network (ANN) model with a multilayer perceptron (MLP) architecture as a well-known method to account for the reliability of SVR or the possible improvement made by SVR over ANN models. The results of this study show that by considering correlation coefficient and some statistical errors the performance of the SVR model slightly improves the ANN porosity predictions.
