The preparation of an amino acid-based surfactant and its potential application as an EOR agent

Most of the synthetic surfactants investigated with the aim of enhanced chemically oil recovery in the literature have environmental drawbacks. In this work, application of an environmentally-friendly synthetic surfactant as an enhanced oil recovery agent is introduced by measuring interfacial tension of water–kerosene systems and wettability alteration of carbonate pellets. For this purpose, an amino acid-based surfactant was initially synthesized using a new synthetic approach which was subsequently confirmed by spectra of Fourier transform infrared and Proton nuclear magnetic resonance. Results showed a value of critical micelle concentration in the range of 9000–9100 ppm for this surfactant. Results also demonstrated a decrease of 38.53% in water–kerosene system interfacial tension and a 17.76% reduction in oil-wetness of the carbonate pellets.     

Kinetic modeling of pyrolysis of three Iranian waste oils in a micro-fluidized bed

Different approaches have been used to convert the waste materials into a clean syngas or other chemicals such as methanol. Among them, pyrolysis is a good candidate to produce the synthesis gas and volatile matters for industrial and refinery applications. In this work, we studied the kinetic and chemical behavior of three Iranian waste oils through a kinetic model and an experimental study. The experiments carried out in a micro-FB reactor, which is a good option for low emissions. Results showed that the reaction temperature and reaction rate are two of the most important factors for maximum conversion level of fuel. Results also showed an optimum value for reaction rate. The modeling results validated against the experimental measurements and found to be in good agreements.     

Effect of metal loading on catalytic activity and selectivity of ZSM-5 zeolite catalyst in conversion of methanol to olefins and aromatics

Effect of ZSM-5 zeolite promotion using potassium, strontium, and cerium metals on conversion of methanol to light olefins and aromatics was investigated. ZSM-5 catalyst was promoted using wet impregnation of metal salts and its activity was tested in methanol to hydrocarbons process under optimum operational condition obtained in our group previously. Zeolite samples were characterized by X-ray powder diffraction, scanning electron microscopy, N₂ adsorption-desorption, acid sites distribution, and strength using temperature programmed desorption of NH₃. Results showed that presence of these metals could significantly affect acid site distribution and therefore influence the catalyst activity and selectivity to olefins and aromatics.     

Correlation of H2S solubility in aqueous MDEA solutions using electrolyte-modified Peng-Robinson plus association equation of state

In this survey, we use the emPR-CPA equation of state to model a ternary system of H₂S solubility in MDEA aqueous solution at different solution compositions, temperatures, pressures, and acid gas loadings. For electrolyte part of the model, MSA theory and the Born term were used. There were 302 experimental data used in this work. To model a ternary system, at first pure components, then binary subsystems, and finally ternary system were modeled using a reactive bubble point pressure calculation method. An overall absolute average deviation percentage equal to 20.85 was achieved so that show the model efficiency for correlation of such systems.     

Adsorptive desulfurization and denitrogenation of model fuel by mesoporous adsorbents (MSU-S and CoO-MSU-S)

Adsorption of heterocyclic sulfur- and nitrogen-containing compounds by mesostructure adsorbent (MSU-S) and its modified form with cobalt oxide is studied using model fuel. The results of characteristic tests (XRD, N₂ adsorption–desorption, FTIR, and SEM) indicate that CoO impregnation causes a negative impact on mesoporous structure, crystalline phase, and particle shape along with a positive effect on surface ion exchange. CoO modification increased the adsorption loadings of DBT and BT to about 33.6% and 45.7%, respectively. For nitrogen compounds adsorption with the model fuel, adsorption loadings of quinoline and carbazole increase by 6.7% and 8.6%, respectively. Data fitting for carbazole, DBT, and BT is achieved better by the Langmuir model than the Freundlich model, and the data of quinoline fitted very well to the Freundlich model for CoO-MSU-S.     

Application of LS-SVM strategy to estimate H2S loading capacity in different ionic liquids and alkanolamines

Favorable properties of aqueous solutions are improved with the addition of different materials for separation of hydrogen sulfide (H₂S). Also, equilibrium data and available equations for solubility estimation of this gas are only valid for specific solutions and limited ranges of temperature and pressure. In this regard, a machine learning model based on Support vector machine (SVM) algorithm is proposed and developed with mixtures containing different amines and ionic liquids to predict H₂S solubility over wide ranges of temperature (298–434.5 K), pressure (13–9319 kPa), overall mass concentration (3.82–100%) and mixture's apparent molecular weight (18.39–556.17 g/mol). The accuracy of the performance of this network was evaluated by regression analysis on calculated and experimental data, which had not been used in network training.     

Modeling of CO2-brine interfacial tension: Application to enhanced oil recovery

Development of reliable and accurate models to estimate carbon dioxide–brine interfacial tension (IFT) is necessary, since its experimental measurement is time-consuming and requires expensive experimental apparatus as well as complicated interpretation procedure. In the current study, feed forward artificial neural network is used for estimation of CO₂–brine IFT based on data from published literature which consists of a number of carbon dioxide–brine interfacial tension data covering broad ranges of temperature, total salinity, mole fractions of impure components and pressure. Trial-and-error method is utilized to optimize the artificial neural network topology in order to enhance its capability of generalization. The results showed that there is good agreement between experimental values and modeling results. Comparison of the empirical correlations with the proposed model suggests that the current model can predict the CO₂–brine IFT more accurately and robustly.     

Effect of dead space on gain and noise ofdouble-carrier-multiplication avalanche photodiodes

The effect of dead space on the statistics of the gain in a double-carrier-multiplication avalanche photodiode (APD) is determined using a recurrence method. The dead space is the minimum distance that a newly generated carrier must travel in order to acquire sufficient energy to become capable of causing an impact ionization. Recurrence equations are derived for the first moment, the second moment, and the probability distribution function of two random variables that are related, in a deterministic way, to the random gain of the APD. These equations are solved numerically to produce the mean gain and the excess noise factor. The presence of dead space reduces both the mean gain and the excess noise factor of the device. This may have a beneficial effect on the performance of the detector when used in optical receivers with photon noise and circuit noise     

Applying FCM-ANFIS algorithm as a novel computational method for prediction of viscosity of bitumen and heavy alkane mixture

Recently the studies expressed that the noticeable number of oil reservoirs in all over the world are heavy oil and bitumen reservoirs. So the importance of enhancement of oil recovery (EOR) processes for heavy oil and bitumen reservoirs is highlighted. The Dilution of the reservoir fluid by solvents such as tetradecane is one of well-known methods for these types of reservoirs which effects oil recovery by decreasing viscosity. In the present study, Fuzzy c-means (FCM) algorithm was coupled with Adaptive neuro-fuzzy inference system (ANFIS) to predict viscosity of bitumen and tetradecane in terms of temperature, pressure and weight percent of tetradecane. The coefficients of determination for training and testing steps were calculated such as 0.9914 and 0.9613. The comparison of results and experimental data expressed that FCM-ANFIS algorithm has great potential for estimation of viscosity of bitumen and tetradecane.     

The impact of geological uncertainty on primary production from a fluvial reservoir

Deposition of fluvial sandbodies is controlled mainly by characteristics of the system, such as the rate of avulsion and aggradation of the fluvial channels and their geometry. The impact and the interaction of these parameters have not received adequate attention. In this paper, the impact of geological uncertainty resulting from the interpretation of the fluvial geometry, maximum depth of channels, and their avulsion rates on primary production is studied for fluvial reservoirs. Several meandering reservoirs were generated using a process-mimicking package by varying several controlling factors. Simulation results indicate that geometrical parameters of the fluvial channels impact cumulative production during primary production more significantly than their avulsion rate. The most significant factor appears to be the maximum depth of fluvial channels. The overall net-to-gross ratio is closely correlated with the cumulative oil production of the field, but cumulative production values for individual wells do not appear to be correlated with the local net-to-gross ratio calculated in the vicinity of each well. Connectedness of the sandbodies to each well, defined based on the minimum time-of-flight from each block to the well, appears to be a more reliable indicator of well-scale production.