Iranian Journal of Oil & Gas Science and Technology
Volume:3 Issue: 3, Summer 2014

Wettability alteration is an important method for increasing oil recovery from oil-wet carbonate reservoirs. Chemical agents like surfactants are known as wettability modifiers in carbonate systems. Oil can be recovered from initially oil-wet carbonate reservoirs by wettability alteration from oil-wet to water-wet condition with adding dilute surfactant and electrolyte solutions. This paper investigates the effects of brine concentration, surfactant concentration, and the pH of injection water on the wettability alteration of carbonate reservoirs by different class of surfactants. Scanning electron microscopy images verified the formation of surfactant layer surfaces and the adsorption of surfactant molecules on the rock. The results revealed that TX-100, as a nonionic surfactant, and CTAB, as a cationic surfactant, were better wettability modifiers than SDS, as an anionic surfactant, for carbonate rocks. At the concentration of 1 wt.% and higher, the contact angle reduction was approximately unchanged. The results also proved that there was an optimum salinity for the maximum wettability alteration by surfactants. Increasing the pH of aging fluid resulted in better wettability alteration by CTAB, while, in the case of SDS, the wettability alteration was reduced. Acidic conditions had a negligible effect on the wetting behavior of TX-100.

The spatial distribution of petrophysical properties within the reservoirs is one of the most important factors in reservoir characterization. Flow units are the continuous body over a specific reservoir volume within which the geological and petrophysical properties are the same. Accordingly, an accurate prediction of flow units is a major task to achieve a reliable petrophysical description of a reservoir. The aim of this paper was core flow unit determination by using a new intelligent method. Flow units were determined and clustered at specific depths of reservoir by using a combination of artificial neural network (ANN) and a metaheuristic optimization algorithm method. At first, artificial neural network (ANN) was used to determine flow units from well log data. Then, imperialist competitive algorithm (ICA) was employed to obtain the optimal contribution of ANN for a better flow unit prediction and clustering. Available routine core and well log data from a well in one of the Iranian oil fields were used for this determination. The data preprocessing was applied for data normalization and data filtering before these approaches. The results showed that imperialist competitive algorithm (ICA), as a useful optimization method for reservoir characterization, had a better performance in flow zone index (FZI) clustering compared with the conventional K means clustering method. The results also showed that ICA optimized the artificial neural network (ANN) and improved the disadvantages of gradient-based back propagation algorithm for a better flow unit determination.

The most costly operation in the oil exploration is the well network drilling. One of the most effective ways to reduce the cost of drilling networks is decreasing the number of the required wells by selecting the optimum situation of the rig placement. In this paper, Balas algorithm is used as a model for optimizing the cost function in oil well network, where the vertical and directional drilling is performed. The model can determine optimal well placement as well as optimal paths to develop the field. The proposed model is implemented in an Iran southern gas field with five drilling rigs used to drill 44 wells from 14 positions on the surface. The results show a 17.4% reduction compared to the proposed cost.

In this paper, the development of a new potassium carbonateon alumina support sorbent prepared by impregnating K2CO3 with an industrial grade of Al2O3 support was investigated. The CO2 capture capacity was measured using real flue gas with 8% CO2 and 12% H2O in a fixed-bed reactor at a temperature of 65 °C using breakthrough curves. The developed sorbent showed an adsorption capacity of 66.2 mgCO2/(gr sorbent). The stability of sorbent capture capacity was higher than the reference sorbent. The SO2 impurity decreased sorbent capacity about 10%. The free carbon had a small effect on sorbent capacity after 5 cycles. After 5 cycles of adsorption and regeneration, the changes in the pore volume and surface area were 0.020 cm3/gr and 5.5 m2/gr respectively. Small changes occurred in the pore size distribution and surface area of sorbent after 5 cycles.

In this work, artificial neural network (ANN) has been employed to propose a practical model for predicting the surface tension of multi-component mixtures. In order to develop a reliable model based on the ANN, a comprehensive experimental data set including 15 ternary liquid mixtures at different temperatures was employed. These systems consist of 777 data points generally containing hydrocarbon components. The ANN model has been developed as a function of temperature, critical properties, and acentric factor of the mixture according to conventional corresponding state models. 80% of the data points were employed for training ANN and the remaining data were utilized for testing the generated model. The average absolute relative deviations (AARD%) of the model for the training set, the testing set, and the total data points were obtained 1.69, 1.86, and 1.72 respectively. Comparing the results with Flory theory, Brok-Bird equation, and group contribution theory has proved the high prediction capability of the attained model.

This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter for the final state estimation. The recent data are recursively utilized to apply wavelet transform and extract the variance of the updated data, which makes it suitable to be applied to both static and dynamic systems corrupted by noisy environments. The method has suitable performance in state estimation in comparison with the other alternative algorithms. A three-tank benchmark system has been adopted to comparatively demonstrate the performance merits of the method compared to a known algorithm in terms of efficiently satisfying signal-tonoise (SNR) and minimum square error (MSE) criteria.