Iranian Journal of Oil and Gas Science and Technology

Abstract Stripper columns are used for sweetening crude oil, and they must hold product hydrogen sulfide content as near the set points as possible in the faces of upsets. Since product    quality cannot be measured easily and economically online, the control of product quality is often achieved by maintaining a suitable tray temperature near its set point. Tray temperature control method, however, is not a proper option for a multi-component stripping column because the tray temperature does not correspond exactly to the product composition. To overcome this problem, secondary measurements can be used to infer the product quality and adjust the values of the manipulated variables. In this paper, we have used a novel inferential control approach base on adaptive network fuzzy inference system (ANFIS) for stripping process. ANFIS with different learning algorithms is used for modeling the process and building a composition estimator to estimate the composition of the bottom product. The developed estimator is tested, and the results show that the predictions made by ANFIS structure are in good agreement with the results of simulation by ASPEN HYSYS process simulation package. In addition, inferential control by the implementation of ANFIS-based online composition estimator in a cascade control scheme is superior to traditional tray temperature control method based on less integral time absolute error and low duty consumption in reboiler. 

Abstract Gravity drainage is the main mechanism which controls the oil recovery from fractured reservoirs in both gas-cap drive and gas injection processes. The liquid bridge formed between two adjacent matrix blocks is responsible for capillary continuity phenomenon. The accurate determination of gas-liquid interface profile of liquid bridge is crucial to predict fracture capillary pressure precisely. The liquid bridge interface profile in the absence and in the presence of gravity is numerically derived, and the obtained results are compared with the measured experimental data. It is shown that in the presence of gravity, fracture capillary pressure varies across the fracture, whereas, by ignoring gravitational effects, a constant capillary pressure is obtained for the whole fracture. Critical fracture aperture which is the maximum aperture that could retain a liquid bridge was computed for a range of liquid bridge volumes and contact angles. Then, non-linear regression was conducted on the obtained dataset to find an empirical relation for the prediction of critical fracture aperture as a function of liquid bridge volume and contact angle. The computation of fracture capillary pressure at different liquid bridge volumes, fracture apertures, and contact angles demonstrates that if the liquid bridge volume is sufficiently small (say less than 0.5 microliters), capillary pressure in a horizontal fracture may reach values more than 0.1 psi, which is comparable to capillary pressure in the matrix blocks. The obtained results reveal that the variation of fracture capillary pressure versus bridge volume (which represents liquid saturation in fracture) obeys a trend similar to the case of matrix capillary pressure. Therefore, the capillary pressure of matrix can be applied directly to fractures considering proper modifications. The results of this study emphasize the importance of capillary continuity created by liquid bridges in the performance of gas-oil gravity drainage in fractured reservoirs.

Abstract In the current work, a framework is presented for amine solvent selection in gas treating process. Since the appropriate decision making in this field affects the capital and operational costs, multi attribute decision making (MADM) techniques were used to rank alternatives. The determination of criteria and alternatives is the most important aspect in the MADM. Criteria were divided into two categories, namely physical and process, and twelve physical indexes and nine process indexes were detected. Mono-ethanol amine (MEA), di-glycol amine (DGA), di-ethanol amine (DEA), di-isopropanol amine (DIPA), and methyl di-ethanol amine (MDEA) are intended as alternatives. The importance of the criteria was expressed by weights, and the weights were determined by the analytic hierarchy process (AHP) method. The traditional Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method was applied to the physical criteria with crisp data. The modified interval TOPSIS technique was used to study the process criteria with interval data. The data of the criteria and alternatives were collected from Ilam Gas Treating Company, and the solution for sour gas sweetening was ranked by the proposed approach. Based on our computations, MDEA was defined as the best amine solvent with an average ranking of 1.5.

Abstract Gasoline obtained from the fractionation of indigenous natural gas condensate has low octane number (78) and is therefore of limited uses. Lead-based octane boosting and catalytic reforming are not the viable methods for many fractionation plants. This study was therefore aimed to develop an inexpensive conceptual alternative method for boosting the octane number of gasoline. Natural gas concentrated in methane having high octane number (more than 100) was absorbed in the gasoline to boost the octane number partially (86). Selective additives i.e. ethanol, tert-butyl alcohol, methylcyclopentane, toluene, iso-octane and xylene were blended first with the gasoline to aid the absorption of natural gas molecules. The loss of absorbed gas molecules from gasoline with the increase in temperature was also observed. It is therefore required to try for avoiding any increase in temperature in the finished gasoline. The developed conceptual method is promising. The findings of this simulation study would be useful for more studies towards the development of an affordable alternative method for fractionation plants for boosting the octane number of gasoline derived from natural gas condensate.

Abstract Currently available polymers as a component of in-situ gels are unsuitable for treating high-temperature/high-salinity reservoirs due to their chemical and thermal degradation. In this study, a new copolymer-based gel system including high molecular weight nanostructured polymers (NSPs) was developed to address the excessive water production problem in reservoirs under harsh conditions. The stability of conventional polymer systems and NSPs was investigated under conditions of 40 days aging at 87000 ppm salinity and 90 °C. Then, gelation time optimization of gel systems composed of NSPs and chromium (III) acetate was performed with regards to the effect of copolymer concentration and copolymer/cross-linker ratio and their interactions during the gelation time. The central composite approach was used to design experiments and build a mathematical model. The analysis of variance (ANOVA) was used to estimate the deviation of the model predictions from the data. The results of stability analysis demonstrated the advantages of NSPs over conventional polymers by a viscosity reduction of 69, 36, and 18% for Flopaam3310, AN105, and NSPs respectively. The model developed for the prediction of gelation time of NSPs gel was significant at a confidence level of 98.6% against the test data. Moreover, it was found that gelation time became longer with a decrease in copolymer concentrations and/or increase in copolymer/cross-linker ratio.

Abstract The influence of increasing the SiO2/K2O molar ratio on the electrochemical action of a waterborne potassium silicate zinc-rich coating was investigated by means of electrochemical impedance spectroscopy (EIS) and corrosion potential (Ecorr) measurements. The EIS results showed that increasing the SiO2/K2O molar ratio in the range of 3.135 to 5 by the addition of nano-SiO2 to the resins improved the resistance of coatings; however, higher molar ratios showed an adverse effect. Moreover, the alkali silicate binder of the sample with a SiO2/K2O molar ratio of 5 was improved by adding 5, 10, and 15 wt.% of acrylic resin and acrylic/styrene copolymer to potassium silicate resin. These formulated coatings were sprayed over carbon steel plates and the adhesion and morphology of these primers were evaluated by pull-off, cross cut, and scanning electron microscopy tests. Electrochemical measurements showed that the sample with a SiO2/K2O molar ratio of 5 had better corrosion properties than the other samples. Adhesion and SEM tests also showed that B1 and C2 with respectively 5 and 10% acrylic derivatives had less holes, cracks, and better adhesive properties.

Abstract Gas injection into carbonate formations is one of the most important activities to protect oil reserves that can guarantee a steady production. On-time injection of enough gas can result in the recovery of billions barrels of oil. In addition, it can preserve a huge amount of gas for the next generations. If the reservoir depth is shallow, or the reservoir fluid has a little amount of intermediate components, the flooding of rich gases is highly recommended. In the designing of a miscible injection process, firstly the minimum miscibility pressure should be measured or determined analytically. In this study, first the PVTi software is implemented to evaluate the miscibility of different injected gas, including carbon dioxide, nitrogen, methane, and different proportion of hydrocarbon gases. Subsequently, E-300 software is used to predict the recovery of the gas injection into the formation under study from one of the Iranian carbonate onshore fields. The investigation of the optimum injection rate as well as finding the proper layer of injection is investigated in details. The results show that the CO₂ flooding after a long natural production period result in higher efficiency than the miscible injection of methane at the early stage of production.

Abstract Fractured reservoirs contain about 85 and 90 percent of oil and gas resources respectively in Iran. A comprehensive study and investigation of fractures as the main factor affecting fluid flow or perhaps barrier seems necessary for reservoir development studies. High degrees of heterogeneity and sparseness of data have incapacitated conventional deterministic methods in fracture network modeling. Recently, simulated annealing (SA) has been applied to generate stochastic realizations of spatially correlated fracture networks by assuming that the elastic energy of fractures follows Boltzmann distribution. Although SA honors local variability, the objective function of geometrical fracture modeling is defined for homogeneous conditions. In this study, after the introduction of SA and the derivation of the energy function, a novel technique is presented to adjust the model with highly heterogeneous data for a fractured field from the southwest of Iran. To this end, the regular object-based model is combined with a grid-based technique to cover the heterogeneity of reservoir properties. The original SA algorithm is also modified by being constrained in different directions and weighting the energy function to make it appropriate for heterogeneous conditions. The simulation results of the presented approach are in good agreement with the observed field data.

Abstract Chemical flooding, especially alkaline/surfactant/polymer flooding, is of increasing interest due to the world increasing oil demand. This work shows the aspects of using alkaline/surfactant/polymer as an enhanced oil recovery method in the porous media having a high dead-end pore frequency with various dead-end pore parameters (such as opening, depth, aspect ratio, and orientation). Using glass micromodels makes it possible to manipulate and analyze the pore parameters and watch through the porous media precisely. The results show that polyacrylamide almost always enhances oil production recovery factor (up to 14% in comparison with brine injection) in this kind of porous media. Except at low concentrations of polyacrylamide and sodium carbonate, sodium dodecyl sulfonate improves oil recovery (even 15% in the case of high polyacrylamide concentration and low sodium carbonate concentration). Increasing alkaline concentration reduces recovery factor except at low concentrations of polyacrylamide and high concentrations of surfactant.

Abstract A computational fluid dynamic (CFD) study of methanol (MeOH) to dimethyl ether (DME) process in an adiabatic fixed-bed reactor is presented. One of the methods of industrial DME production is the catalytic dehydration of MeOH. Kinetic model was derived based on Bercic rate. The parameters of this equation for a specific catalyst were tuned by solving a one-dimensional homogenous model using MATLAB optimization module. A two-dimensional CFD simulation of the reaction is demonstrated and considered as numerical experiments. A sensitivity analysis was run in order to find the effect of temperature, pressure, and WHSV on the reactor performance. Good agreement was achieved between bench experimental data and the model. The results show that the maximum conversion of reaction (about 85.03%) is obtained at WHSV=10 h^-1 and T=563.15 K, whereas the inlet temperature has a greater effect on methanol conversion. Moreover, the effect of water in inlet feed on methanol conversion is quantitatively studied. It was concluded that the results obtained from CFD analysis give precise guidelines for further studies on the optimization of reactor performance.

Abstract The mathematical modeling of fracture networks is critical for the exploration and development of natural resources. Fractures can help the production of petroleum, water, and geothermal energy. They also greatly influence the drainage and production of methane gas from coal beds. Orientation and spatial distribution of fractures in rocks are important factors in controlling fluid flow. The objective function recently developed by Masihi et al. 2007 was used herein to generate fracture models that incorporate field observations. To extend this method, simulated annealing, genetic, and tabu search algorithms were employed in the modeling of fracture networks. The effectiveness of each algorithm was compared and the applicability of the methodology was assessed through a case study. It is concluded that the fracture model generated by simulated annealing is better compared to those generated by genetic and tabu search algorithms.

Abstract 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.

Abstract The current paper investigates the influence of packet losses in network control systems (NCS’s) using the model predictive control (MPC) strategy. The study focuses on two main network packet losses due to sensor to controller and controller to actuator along the communication paths. A new Markov-based method is employed to recursively estimate the probability of time delay in controller to actuator path and a generalized predictive control (GPC) method is proposed to compensate the effect of big network time-delay, which leads to packet loss. The proposed methods and algorithms have been evaluated using a practical Smar fieldbus pilot plant to judge the efficiency of the foregoing algorithms. The obtained results clearly demonstrate the superiorities of the proposed control scheme with respect to standard MPC algorithm.

Abstract In this study, liquid-liquid extraction process in a Kuhni extraction column was modeled and simulated. A non-equilibrium dynamic model was developed for modeling liquid-liquid extraction processes based on a rate-based model. The model equations are inclusive of partial and ordinary differential equations which were discretized in column height direction. The population balance model was used for the calculation of droplet size distribution in the dispersed phase and the column hydrodynamic parameters. The equations were solved simultaneously through the finite difference method and the numerical method of lines. Experimental data on a bench scale Kuhni extraction column was used to evaluate the simulation results. The average correlation coefficient error of the mean diameter of the dispersed phase and mass transfer in various operating conditions are less than 2% 4 % respectively. A comparison between the experimental data and the simulation results proves the better productivity of the presented non-equilibrium dynamic model.

Abstract Designing drilling fluids for drilling in deep gas reservoirs and geothermal wells is a major challenge. Cooling drilling fluids and preparing stable mud with high thermal conductivity are of great concern. Drilling nanofluids, i.e. a low fraction of carbon nanotube (CNT) well dispersed in mud, may enhance the mixture thermal conductivity compared to the base fluids. Thus, they are potentially useful for advanced designing high temperature and high pressure (HTHP) drilling fluids. In the present study, the impacts of CNT volume fraction, ball milling time, functionalization, temperature, and dispersion quality (by means of scanning electron microscopy, SEM) on the thermal and rheological properties of water-based mud are experimentally investigated. The thermal conductivities of the nano-based drilling fluid are measured with a transient hot wire method. The experimental results show that the thermal conductivity of the water-based drilling fluid is enhanced by 23.2% in the presence of 1 vol% functionalized CNT at room temperature; it increases by 31.8% by raising the mud temperature to 50 °C. Furthermore, significant improvements are seen in the rheological properties—such as yield point, filtration properties, and annular viscosity—of the CNTmodified drilling fluid compared to the base mud, which pushes forward their future development.

Abstract Reservoir models with many grid blocks suffer from long run time; it is hence important to deliberate a method to remedy this drawback. Usual upscaling methods are proved to fail to reproduce fine grid model behaviors in coarse grid models in well proximity. This is attributed to rapid pressure changes in the near-well region. Standard permeability upscaling methods are limited to systems with linear pressure changes; therefore, special near-well upscaling approaches based on the well index concept are proposed for these regions with non-linear pressure profile. No general rule is available to calculate the proper well index in different heterogeneity patterns and coarsening levels. In this paper, the available near-well upscaling methods are investigated for homogeneous and heterogeneous permeability models at different coarsening levels. It is observed that the existing well index methods have limited success in reproducing the well flow and pressure behavior of the reference fine grid models as the heterogeneity or coarsening level increases. Coarse-scale well indexes are determined such that fine and coarse scale results for pressure are in agreement. Both vertical and horizontal wells are investigated and, for the case of vertical homogeneous wells, a linear relationship between the default (Peaceman) well index and the true (matched) well index is obtained, which considerably reduces the error of the Peaceman well index. For the case of heterogeneous vertical wells, a multiplier remedies the error. Similar results are obtained for horizontal wells (both heterogeneous and homogeneous models).

Abstract A value chain is a series of events that takes a raw material and with each step adds value to it. Global interest in the application of natural gas (NG) in production and transportation has grown dramatically, representing a long-term, low-cost, domestic, and secure alternative to petroleum-based fuels. Many technological solutions are currently considered on the market or in development, which address the challenge and opportunity of NG. In this paper, a decision support system (DSS) is introduced for selecting the best fuel to develop in the value chain of NG through four options, namely compressed NG (CNG), liquefied NG (LNG), dimethyl ether (DME), and gas-to-liquids (GTL). The DSS includes a model which uses the technique for order performance by similarity to ideal solution (TOPSIS) to select the best fuel in the value chain of NG based on the attributes such as market situations, technology availability, and transportation infrastructure. The model recommends some key guidelines for two branches of countries, i.e. those which have NG resources and the others. We believe that applying the proposed DSS helps the oil and gas/energy ministries in a most effective and productive manner dealing with the complicated fuel-related production and transportation decision-making situations.

Abstract In the present business atmosphere, an organization should be able to respond to environmental needs occasioned by rapid and dynamic evolution as quickly as possible. It is obvious that such ability is impossible without a proper strategic approach, strategic thinking, and a suitable management accounting system. This study attempts to investigate the relationships between strategy, management accounting systems, and the performance of Iranian petrochemical companies under the framework of contingency theory. It is assumed that matches between organizational strategies and the content variables of organizational structure in controlling environments such as management accounting systems could produce an optimal level of performance. The statistical population of the study is Iranian petrochemical private companies, and the required information has been gathered by questionnaire the internal reliability of which is confirmed by a Cronbach's alpha coefficient of 83.4%. Structural equation modelling with LISREL software has been used for data analysis and hypotheses testing. The results not only confirm the direct association between strategy, management accounting systems, and company performance, but also support the effect of management accounting system as a mediating variable on the relationship between strategy and performance. According to the results, it might be concluded that contingency theory postulates are applicable to the Iranian petrochemical industry, and this conclusion may shed some light on the way in which these companies are managed and controlled.

Abstract The Taguchi design of experiments (DOE) approach is adopted here to evaluate the impact of
effective factors such as nanoparticles type, nanoparticles to model solution mass ratio, asphaltene
structure, and temperature on asphaltene adsorption equilibrium. Herein, the toluene-asphaltene
solution model is applied. Three commercially nanoparticles (SiO2, Al2O3, and TiO2) are used.
Asphaltene characterizations are carried out by X-ray diffraction (XRD) analysis. It is found that the
nanoparticle type and asphaltene structure with a respective influence of 48.5% and 3.11% have the
maximum and minimum contribution on the amount of adsorbed asphaltene at the selected levels
respectively. Aluminum oxide nanoparticle has the maximum and silicon oxide nanoparticle shows
the minimum adsorption. The temperature has no statistical significance. Asphaltenes with higher
aromaticity have more tendencies for adsorption on nanoparticles.

Abstract Different amounts of nanoclay were incorporated into the acrylic resin matrix at 0, 1, 3, and 5 wt.% loadings. The coatings were applied on low carbon steel plates. Optical microscopy, sedimentation test, transmission electron microscopy, and X-ray diffraction were employed to investigate the dispersion of nanoclay in matrix. The corrosion resistance of coatings was evaluated by electrochemical impedance spectroscopy, polarization measurement, and salt spray test. In addition, pull-off and cross-cut tests were used for the assessment of coating adhesion to the substrate. The results indicated that the anti-corrosive properties of the acrylic resin were obviously increased by the addition of nanoclay. The nanocomposite coatings containing 3 wt.% clay showed the best corrosion resistance. Finally, the nanocomposites containing 1 and 3 wt.% showed the highest adhesion to the substrate.

Abstract Random or incoherent noise is an important type of seismic noise, which can seriously affect the quality of the data. Therefore, decreasing the level of this category of noises is necessary for increasing the signal-to-noise ratio (SNR) of seismic records. Random noises and other events overlap each other in time domain, which makes it difficult to attenuate them from seismic records. In this research, a new technique is produced, by joining FX deconvolution (FXD) and a special kind of median filter in order to suppress random noise from seismic records. The technique is operated in some stages; firstly, FXD is tried to eliminate the Gaussian noise, and the median filter is fixed to diminish the spike-like noise. The synthetic dataset and field data examples (from an oil field in the southwest of Iran) have been employed to demonstrate that random noise reduction can be attained, while the signal content will not be destroyed considerably. The final results indicate the authority of the proposed strategy in suppressing random noises, whereas signal information is almost protected during the filtering.

Abstract In this study, mass transfer coefficients (MTC’s) of natural gas components during hydrate formation are reported. This work is based on the assumption that the transport of gas molecules from gas phase to aqueous phase is dominant among other resistances. Several experiments were conducted on a mixture of natural gas at different pressures and temperatures and the consumed gas was monitored and measured over time. The driving force is the difference between the solubility of hydrate former components at operating pressure and the corresponding equilibrium pressure. It was found that MTC is a function of pressure and temperature during hydrate growth stage. Consequently, an equation was proposed to calculate the mass transfer coefficient based on the experimental data.

Abstract In the past 30 years, electroless nickel (EN) plating has grown to such proportions that these coatings and their applications are now found underground, in outer space, and in a myriad of areas in between. Moreover, in order to further improve the mechanical and tribological properties of the nickel-phosphorous (Ni-P) coatings, Ni-P/PTFE composite coatings can be obtained, which provides even greater friction behavior and lubricity than the one naturally occurring in the nickel-phosphorous alloy deposit. In this paper, The Ni-P-PTFE coating was deposited on mild carbon steel surface via electroless deposition process. The friction behavior and wear mechanisms of Ni-P-PTFE nanocomposite coating were studied at different concentrations of PTFE. Frictional behavior was examined using a pin on disk wear test method. Surface morphology and worn surface was evaluated using field emission scanning electron microscopy (FESEM) and energy dispersive spectroscopy (EDS) analysis. The results showed that the incorporation of PTFE nanoparticles can reduce the wear rate of Ni-P coating from 33.07×10-6 mm3/Nm to 12.46×10-6 mm3/Nm for the Ni-P PTFE containing 10 g/l PTFE and decrease the friction coefficient from 0.64 to 0.2. Thus the tribological behavior of Ni-P coating is much improved in the presence of PTFE nanoparticles and 10 g/l is the optimized concentration of PTFE in the electroless bath.

Abstract In steel production plants, such as those manufacturing sheets, pipes, and round bars, raw materials are annealed in preheating furnaces at approximately 1200 °C before undergoing hot deformation. Substantial oxidation and loss of raw steel materials occur in preheating furnaces, resulting in significant economic losses. A potential solution to reduce losses in this scenario is the application of protective ceramic coatings. This research investigates the effect of a ceramic coating based on Al₂O₃–SiO₂ on the oxidation behavior of steel sheets. The industrial-scale impact of the coating on the oxidation of steel slabs is also examined. The coating was applied using a spray method with slurry ceramic materials dispersed through a compressed air flow. Thickness measurement tests, scanning electron microscopy, and energy dispersive X-ray spectroscopy (EDS) analysis were conducted to evaluate the kinetics, microstructure, and oxidation behavior of the coatings. The findings indicated that the oxidation kinetics for uncoated steel sheets followed a parabolic trend, while the kinetics for ceramic-coated samples exhibited a slower logarithmic behavior. The application of the coating resulted in a reduction of the oxide layer thickness by less than 30% compared to the uncoated samples, attributed to a lower diffusion coefficient in the coated samples. Applying the ceramic coating on ST52 slabs in industrial tests led to a significant reduction in the oxide layer thickness and a more straightforward peel of the oxide layers. This showed that using such ceramic coating for materials in preheating furnaces could effectively reduce oxidation losses and enhance the mechanical quality of final products.

Abstract Packed bed reactors have many applications in different industries such as chemical, petrochemical, and refinery industries. In this work, the effects of some parameters such as the shape and size of particles, bed size, and bed length on the hydrodynamics of the packed beds containing three spherical, cylindrical, and cubic particles types are investigated using CFD. The effect of the combination of three particles types in a packed bed was also simulated. The simulation results show that flow channeling occurs in some parts of the bed which are not suitably covered by particles. It was also seen that flow channeling in the packed bed with cubic particles are more than those containing spherical and cylindrical particles. According to the CFD simulations, wake and vortex flows are created in all the beds, and the shape of particles affects these phenomena. The comparison of the pressure drop created in the packed beds indicates that the pressure drop in the packed beds having three particle types is lower than the packed beds containing only spherical, cylindrical, or cubic particles. Finally, the numerical results were compared with empirical correlations in the literature and showed good agreement.