Petroleum Engineering
Mohammad reza Talaghat; Ahmad Reza Bahmani
Abstract
Several techniques have been used for sand production control in sandstone reservoirs.The main objective of this research is to present a suitable resin to be used as a consolidation agent in oil reservoirs. To achieve this purpose, urea-formaldehyde resin, phenol-formaldehyde resin, and modified urea-formaldehyde ...
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Several techniques have been used for sand production control in sandstone reservoirs.The main objective of this research is to present a suitable resin to be used as a consolidation agent in oil reservoirs. To achieve this purpose, urea-formaldehyde resin, phenol-formaldehyde resin, and modified urea-formaldehyde resin were selected to be studied. Core samples were made by the sand sample provided from the oil fields of southern parts of Iran with an average absolute permeability of 500-600 mD and an average porosity of 15-20% combined with various percentages of each resin. The core samples are tested for permeability, porosity, and compressive strength measurement. The results show that in the consolidation process with resin, modified urea-formaldehyde resin, as a consolidating agent, is more suitable than the other two types of resin. The consolidated sand samples of this resin had a compressive strength between 3100 and 4150 psi, permeability between 980 and 6823 mD, and porosity between 8 and 98%.
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.
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.