Enhancement of Heavy Oil Recovery by Nanoparticle/Microwave Application

The primary heavy oil recovery is low due to the high viscosity and low mobility; hence, conventional thermal enhanced oil recovery methods such as steam flooding are widely applied to increase the oil production. New unconventional method such as microwave assisted gravity drainage (MWAGD) is under study the change the viscosity of the oil by microwave radiation. Different challenges such as heat loss and low efficiency are faced in unconventional thermal recovery methods especially in deep reservoirs. To improve the performance of unconventional methods, nanotechnology can play an important role. Nanomaterials due to their high surface to volume ratio, more heat absorbance, and more conductivity can be used in a novel approach called nanomaterial/microwave thermal oil recovery. In this work, several nanofluids prepared from nanoparticles such as γ-Alumina (γ-Al2O3), Titanium (IV) oxide (TiO2), MgO, and Fe3O4 were used to enhance the oil viscosity reduction in the porous media under MWAGD mechanism. Our tests showed that adding nanoparticles can increase the absorption of microwave radiation in the oil/ water system in the porous media. The magnitude of this increase is related to the type, particle size distribution in base fluid and, concentration of nanoparticles. Aluminum oxide nanoparticle was found to have the greatest effect on thermal properties of water. For example, only 0.05 wt.% of this nanoparticle, improves the alteration in temperature of water for around 100%. This change can affect the oil recovery and changed it from 37% to more than 40% under MWAGD. Hence, our experiments showed that besides other applications of nanotechnology in enhance oil recovery, heavy oil recovery can also be affected by nanomaterials

Nanoparticles-assisted surface charge modification of the porous medium to treat colloidal particles migration induced by low salinity water flooding

Abstract not availableDanial Arab, Peyman Pourafshar

A novel method to model water-flooding via transfer function approach

Abstract Water flooding is one of the most economical methods to increase oil recovery. To maximize oil recovery during water-flooding, it is essential to provide a forecast of reservoir performance. Hence, various methods are used to simulate reservoirs. Although grid-based simulation is the most common and accurate method, time-consuming computation and demand for large quantities of data restrict the use of this method. This study presents the development of a new method to predict the performance of water injection based on Transfer Functions (TF). This method is faster since it requires less data and the only requirements are injection and production rates. In this method, it is assumed that a reservoir consists of a combination of black boxes (TFs). The order and arrangement of the TFs are chosen based on the physical condition of the reservoir which is ascertained by checking several cases. The injection and production rates act as input and output signals to these black boxes, respectively. After analyzing input and output signals, unknown parameters of TFs are calculated. Then, it is possible to predict the reservoir performance. Different cases are employed to validate the derived model. The simulation results show a good agreement with those obtained from common grid-based simulators. In addition, we found out that the TF parameters depend on the characteristics and the pattern of different sections of the reservoir. This method is a rapid way to simulate water-flooding and could be a new window to the future of fast simulators. It enables prediction of the performance of water-flooding and optimization of oil production by testing different injection scenarios. The method also provides key parameters such as well connectivity.Mohammad Sayyafzadeh, Peyman Pourafshary, Fariborz Rashid

Mathematical modeling of colloidal particles transport in the medium treated by nanofluids: deep bed filtration approach

A deep bed filtration model has been developed to quantify the effect of nanoparticles (NPs) on mitigating fines migration in porous media. The filtration coefficients representing the total kinetics of particles capture were obtained by fitting the model to the laboratory data. Based on the optimum filtration coefficients, the model was utilized to history match the particle concentration breakthrough profiles observed in twelve core flood tests. In the flooding experiments, the effect of five types of metal oxide NPs, γ-Al2O3, CuO, MgO, $\hbox {SiO}_{2}$, and ZnO, on migrating fines were investigated. In each test, a stable suspension was injected into the already NP-treated core and effluents’ fines concentration was measured based on turbidity analysis. In addition, zeta potential analysis was done to obtain the surface charge (SC) of the NP-treated medium. It was found that the presence of NPs on the medium surface results in SC modification of the bed and as a result, enhances the filter performance. Furthermore, the ionic strength of the nanofluid was recognized as an important parameter which governs the capability of NPs to modify the SC of the bed. The remedial effect of NPs on migrating fines is quantitatively explained by the matched filtration coefficients. The SC of the medium soaked by γ-Al2O3 nanofluid is critically increased; therefore, the matched filtration coefficient is of remarkably high value and as a result, the treated medium tends to adsorb more than 70 % of suspended particles. The predicted particle concentration breakthrough curves well matched with the experimental data.Danial Arab, Peyman Pourafshary, Shahaboddin Ayatollah

Application of nanofluid to control fines migration to improve the performance of low salinity water flooding and alkaline flooding

Abstract not availableYasamanAssef, DanialArab, PeymanPourafshar

Optimization of waterflooding performance in a layered reservoir using a combination of capacitance-resistive model and genetic algorithm method

Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. In many applications in reservoir modeling and management, there is a need for rapid estimation of large-scale reservoirs. The capacitance-resistive model (CRM), regarded as a promising rapid evaluator of reservoir performance, has recently been used for simulation of single-layer reservoirs. Injection and production rates are considered as input and output signals in this model. Connections between the wells and the effects of injection rates on production rates are calculated based on these signals to develop a simple model for the reservoir. In this study, CRM is improved to model a multilayer reservoir and is applied to estimate and optimize waterflooding performance in an Iranian layered reservoir. In this regard, CRM is coupled with production logging tools (PLT) data to study the effects of layers. A fractional-flow model is also coupled with the developed CRM to estimate oil production. Genetic algorithm (GA) method is used to minimize the error objective function for the total production history and oil production history to evaluate model parameters. GA is then used to maximize oil production by reallocating the injected water volumes, which is the main purpose of this research. The results show that our fast method is able to model liquid and oil production history and is in good agreement with available field data. Taking into account the reservoir constraints, the optimal injection schemes have been obtained. For the proposed injection profile, the field hydrocarbon production will increase by up to 1.8% until 2016. Also, the wells will reach the water-cut constraint 2 yr later than the current situation, which increases the production period of the field.Azadeh Mamghaderi, Alireza Bastami, Peyman Pourafshar

Heterogeneity effect on non-wetting phase trapping in strong water drive gas reservoirs

Abstract not availableMohammad Rezaee, Behzad Rostami, Peyman Pourafshar

Remediation of colloid-facilitated contaminant transport in saturated porous media treated by nanoparticles

First online: 17 May 2013Facilitation of contaminant transport in porous media due to the effect of indigenous colloidal fine materials has been widely observed in laboratory and field studies. It has been explained by the increase in the apparent solubility of low soluble contaminants as a result of their adsorption on the surface of fine particles. Attachment of colloidal fine particles onto the rock surface could be a promising remedy for this challenge. In this experimental study, the effect of five types of metal oxide nanoparticles, γ-Al2O3, ZnO, CuO, MgO, and SiO2, on suspension transport was investigated. In several core flooding tests, different nanofluids were used to saturate the synthetic porous media. Subsequently, after sufficient soaking time, the suspension was injected into the treated porous media. Analysis of the effluent samples’ concentration by Turbidimeter apparatus demonstrated that the presence of nanoparticles on the rock surface resulted in a significant reduction in fine concentrations in the effluent samples compared with non-treated media; ZnO and γ-Al2O3 demonstrated the best scenarios among the tests performed in this study. In order to characterize the surface properties of the treated porous media, the zeta potential of the surface was measured. Results showed that the treated porous media acts as a strong adsorbent of fine particles, which are the main carrier of contaminants in porous media. These findings were quantitatively confirmed by calculation of the total energy of interaction between the fine particles and rock surface using the Derjaguin–Landau–Verwey–Overbeek theory.D. Arab, P. Pourafshary, Sh. Ayatollahi, A. Habib

A new method to forecast reservoir performance during immiscible and miscible gas-flooding via transfer functions approach

Document ID: 145384-MSAbstract In order to improve oil recovery during gas flooding, it is crucial to afford an accurate estimation of future performance. So far, different approaches have been developed to present forecasting. The most common reservoir simulator, grid-based, which has the highest accuracy, suffers from some weaknesses; time-consuming computation and also need for large quantity of data. Sometimes, a quick overview of reservoir performance is adequate or all required data are not accessible. Therefore, in this study a fast simulator is introduced to provide a quick overview with the minimum amount of data. In this study, the development of a method based on transfer functions (TF) is presented to model immiscible and miscible gas flooding. TF is a mathematical representation in Laplace domain which demonstrates the relation between the input and output signals of a system. In the proposed method, reservoirs are modeled with a combination of TFs. The order and arrangement of the TFs are chosen based on the physical conditions of the reservoir which are ascertained by checking several cases. Injection and production mass rates act as input and output signals respectively. TF parameters are calculated using history matching. Also, a fractional flow model is introduced and coupled with the TF system to obtain oil production rate as the final output. In an attempt to validate the approach, it is required to compare the results with the grid-based method. Six different synthetic cases are constructed and used to validate the developed approach. The results state a good agreement with those obtained from the numerical simulators. This approach is a quick way to predict gas flooding performance with minimum amount of data, production and injection rates data are the only requirements. It can be a new window for the future of fast simulators. It provides estimations with plausible certainty. On the other hand, the analytical solution of method enables its utilization in finding optimum rates for gas injection in a short period of time. The method also presents some key parameters such as well connectivity. It is fair to state that the use of the model is limited to situations when a rapid estimation is looked for and/or adequate data is not accessible.Mohammad Sayyafzadeh, Azadeh Mamghaderi, Peyman Pourafshary, and Manouchehr Haghigh

Disjoining pressure and gas condensate coupling in gas condensate reservoirs

Pore-scale coupled flow of gas and condensate is believed to be the main mechanism for condensate production in low interfacial tension, IFT, gas condensate reservoirs. While coupling enhances condensate flow due to transport of condensate lenses by the gas, it dramatically reduces gas permeability by introducing capillary resistance against gas flow. In this study, a dynamic wetting approach is used to investigate the effect of viscous resistance, IFT and disjoining pressure on pore-scale coupling of gas and condensate. Disjoining pressure arises from van der Waals interactions between gas and solid through thin liquid films, e.g., condensate films on pore walls. Low values of IFT and small pore diameters, as involved in many gas condensate reservoirs, give rise to importance of disjoining pressure. Calculations show that disjoining pressure postpones gas condensate coupling to higher condensate flow fractions-from about, ., for vanishing disjoining effect to more than, ., for strong disjoining effect. Results also suggest that strong disjoining effect will result in higher gas relative permeability after coupling. Finally, the positive rate effect on gas permeability is only observed when disjoining effects are weakMohammad Mohammadi-Khanaposhtani, Alireza Bahramian, Peyman Pourafshar