Iranian Journal of Oil & Gas Science and Technology
Volume:3 Issue: 2, Spring 2014

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.

Wettability alteration is one of the most important methods for oil recovery from sandstone and carbonate reservoirs. The effects of salinity, pH, temperature, and chemicals such as surfactants and fatty acids on the alteration of the wettability were described in previous studies. In recent years, attention has been directed to nanoparticles as a wettability alteration agent. The effect of some nanoparticles on the wettability alteration and oil recovery of sandstone and a few carbonate reservoir rocks have been investigated in several works. In this study, the effect of γ-Al2O3 on the wettability alteration of one of the Iran carbonate reservoirs is presented. The results show that the adsorption of γ-Al2O3 nanoparticles on the calcite surface changes the wettability from oil-wet to water-wet. At a γ- Al2O3 nanofluid concentration of 0.5 wt.%, the maximum change in contact angle was observed. It was observed that the oil recovery increased by 11.25% when 0.5 wt.% γ-Al2O3 nanofluid was injected into the core sample in a tertiary mode. This work illustrates the successful application of gamma alumina nanoparticle in enhancing oil recovery in carbonate rocks through the wettability alteration of rock surfaces.

Nanotechnology has the potential to introduce revolutionary changes to several areas of oil and gas industry such as exploration, production, enhanced oil recovery, and refining. In this paper, the effect of different concentrations of Fe2O3 nanoparticles as a catalyst on the heavy oil viscosity at various temperatures is studied. Furthermore, the effect of a mixture of Fe2O3 nanoparticles and steam injection on heavy oil recovery is studied in laboratory. The experimental tests show that some of these nanoparticles decrease the heavy oil viscosity less than 50% at certain concentrations at different temperatures. The reason for this viscosity reduction is that, similar to a catalyst, thesenanoparticles activate some reactions. Our results of steam injection tests show that the injection of a Fe2O3 nanoparticle mixture increases heavy oil recovery because of cracking reactions which crack the C-S, C=C, and C≡C bonds of the heavy components of heavy oil and change them to light components.

The main factor affecting the economics of foam-assisted water alternative gas (FAWAG) process is the loss of foaming agent by adsorption onto reservoir rocks. In this study, the effects of phases, surfactant concentration, salinity, adsorbents, and sacrificial agent on adsorption density were investigated by special adsorption experiments. Moreover, a series of FAWAG tests were performed to examine the effect of injection rates on final adsorption density and adsorption variation during the test. A clean and fast spectrophotometric method was used for the determination of sodium dodecyl sulfate (SDS) concentration based on the formation of an ion-pair, SDS-Safranin O. Higher SDS adsorption was observed at the first cycle of FAWAG flooding. FAWAG injection rate had no noticeable effect on the adsorption density. However, using high injection rate decreased the possibility that gas faces surfactant, and thereby reducing the ultimate oil recovery. The presence of CLS increases the ultimate oil recovery slightly, while it decreases the adsorption density of SDS by 22%.

In this work, artificial neural network (ANN) was utilized to develop a new model for the prediction of the kinematic viscosity of petroleum fractions. This model was generated as a function of temperature (T), normal boiling point temperature (Tb), and specific gravity (S). In order to develop the new model, different architectures of feed-forward type were examined. Finally, the optimum structure with three hidden layers was selected. The optimum structure had five, four, and two neurons in the first, second, and third layers respectively. To prevent over-fitting problem, 70% of the experimental data were used to train and validate the new model and the remaining data which did not participate in learning process was utilized to test the ability of the new model for the prediction of the kinematic viscosity of petroleum fractions. The results showed that the predicted/calculated and experimental data are in good agreement. The average absolute relative deviation (AARD) of the new model was 1.3%. Finally, the results were compared with an Eyring-based model (Soltani et al.’s work); it was shown that, based on the reported results by the authors, the accuracy of both model were in the same order.

Arguably, the natural gas transmission pipeline infrastructure in Iran represents one of the largest and most complex mechanical systems in the world. The optimization of large gas trunk lines known as IGAT results in reduced fuel consumption or higher capability and improves pipeline operation. In the current study, a single-objective optimization was conducted for Khormoj compressor station on the Iranian gas trunk line V (IGAT5). The system consists of over 504 kilometers of 56-inch pipeline from South Pars to Aghajari. This system passes through a tortuous terrain with changes in elevation which makes the optimization process even more challenging. Genetic algorithm (GA) was used in this optimization along with detailed models of the performance characteristics of compressors. The results show that in stations having the same compressor in parallel the minimum power (energy) consumption is reached when split flow in all the compressors is the same.