مجله پژوهش نفت
پیاپی 113 (مهر و آبان 1399)

One of the most important processes for avoiding global warming and increasing the heating value of natural gas is adsorption and separation of carbon dioxide and nitrogen using zeolite. In this study, experimental results of adsorption of methane, carbon dioxide and nitrogen by zeolite 13X was assessed using artificial neural network. The temperature and pressure was considered as inputs, and adsorption capacity was considered as the output of system. In all models, Levenberg-Marquardt back-propogation was used for training of the network. To find the optimum transfer function in hidden and output layers and optimum number of neurons, coefficient of determination, sum of squared errors, mean square error were calculated. Optimized number of neurons for methane, carbon dioxide and nitrogen was obtained 10, 10, and 15 respectively. Moreover, the best transfer functions were Logsig and Tansig for methane, Logsing and Pureline for carbon dioxide and nitrogen for hidden and output layers. In the end, average deviation percentage for predicted results with neural network was compared with the results obtained by Langmuir and dependent on temperature sip models. It indicates that neural network has high accuracy in comparison with other two models.

Remote sensing technology is one of the methods to detect oil spill, especially in a large region. The high resolution satellite images can be used in primary steps of oil exploration. The aim of this study was to detect the oil spill events of June 2, and 10, 2015, around oil platforms, northern Persian Gulf, using Landsat 7 and Landsat 8 thermal infrared imagery. The applied method was based on measuring the sea surface temperature using the thermal bands. Results showed that the sea surface covered by oil spill has lower temperature than surroundings. It indicates that the derived sea surface temperature just reflects the mentioned status, and this method can be used to detect the potential oil basins. Finally, the results were verified using the Kappa coefficient as part of the confusion matrix; in addition, the amounts of the Kappa coefficient were estimated 0.98 and 0.85 for bands 10 and 11 of Landsat 8, and 0.92 for band 6 of Landsat 7 respectively. These values confirmed the accuracy of results with acceptable coefficients.

In this study, after separating oil and grease from gasfield produced water, removal of organic pollutants has been investigated to reduce its chemical oxygen demand (COD) from 9500 mg/L to the limits of agriculture irrigation. Since the initial electrical conductivity of the wastewater was too high (6300 µS/cm), evaporation process was used to decrease its dissolved solids content. As results of evaporation, electrical conductivity and COD have been reduced to 1100 µS/cm and 750 mg/L respectively. Afterwards, photoelectrocatalysis as an advanced oxidation process was applied to treat distilled wastewater from evaporation step because of COD content of less than 1000 mg/L. In the photoelectrocatalytic method, “boron carbon nitrite nanomaterial” was used as photocatalyst in a coil type microreactor with surface-to-volume ratio of about 3000 m2/m3, which it causes in rapid oxidation of organics. After fabricating the microreactor, the effect of different parameters on COD removal has been investigated. It has been found out that optimum conditions to remove of 80% COD are accordance with pH = 3, potential difference 20 V and electrical conductivity of 2500 µS/cm, so that COD is reached about 150 mg/L which is suitable for agricultural irrigation.

Using sensitivity analysis is one of the effective methods to simplify numerical models. There are a lot of parameters in models constructed for the simulation of oilfields. These parameters do not have the same impact on the model›s output. Therefore, utilization of the sensitivity analysis methods help that nonsignificant variables can be identified and kept at nominal values in order to save time and cost. According to studies, favorable method in sensitivity analysis is a dimensionless, quantitative, global, and model-free method. In this study, Sobol method has been selected as a desirable method for sensitivity analysis of production parameters. This method is applied to the surface model of one of Iran›s oilfields and it is characterized that parameters such as choke size, flowline diameter and pump frequency have the greatest effect on the oil production rate.

Owing to the catalytic properties of ionic liquids (ILs), they might break down the asphaltene and resin molecule structures, and consequently, they will result in upgrading the heavy oil cuts. The superiority of using ILs to other techniques of upgrading is (1) the low operating condition and (2) the lack of negative effects of sulfur compounds and heavy metals on the catalytic role of ionic liquids. In this study, the capability of conversion the heavy oil cuts into light cuts via an IL([OMIM][Cl]) and two modified ILs, which have been modified with iron and aluminum salts, has been investigated. The viscosity, asphaltic percentage and sulfur content were analyzed for characterization of the physicochemical properties of the heavy oil cuts. Furthermore, the influences of several parameters such as the type of heavy oil cuts, the temperature of reaction, the residence time and the percentage of utilization of modified ILs on the quality of heavy oil cuts were evaluated by Minitab software. The results indicated that the presence of Fe and Al in the structure of modified ILs ([OMIM][FeCl3] and [OMIM][AlCl3]) could significantly enhance the capability of these ionic liquids in improving the quality of heavy oil cuts. This resulted in decreasing the value of viscosity and the pollutants containing sulfur and nitrogen compounds in heavy oil cuts. It was observed that the percentage of utilization of modified ILs was the most effective in upgrading of heavy oil cuts. Based on the results at the best condition, the modified ILs of [OMIM][FeCl3] and [OMIM][AlCl3] could reduce 74.44% and 41.80% of the viscosity value and 57.17% and 68.46% of the asphaltene content in the heavy oil cuts respectively. Moreover, the sulfur content in the heavy oil cuts decreased from 2.9% to 1.43% and 0.95% respectively. In the same operating conditions, IL modified with [AlCl3] showed the better performance than one modified by [FeCl3] in increasing the quality of heavy oil cuts. This relative superiority can be attributed to the high value of acidity of AlCl3 in comparison with FeCl3 and the closeness of the binding energy level of Al to sulfur. In conclusion, in this study, it is illustrated that the modified ILs can be considered to be a suitable approach for upgrading the quality of heavy oil cuts at moderate condition.

Asphaltene precipitation/deposition in reservoir formation has detrimental influences on the oil production. The stability of w/o emulsions, the high viscosity of oil, and unfavorably changing the reservoir rock properties are induced in part by the asphaltene instability. Nanoparticles exhibit acceptable capacities for the adsorption and control of the precipitation of asphaltene. In this study, in-situ synthesis of iron oxide nanoparticles in reservoir oils has been followed to evaluate the effects of the thus-prepared nanoparticles on the control of asphaltene damage in tight carbonate core plugs. Model oil has been prepared by dissolving dead oil in a toluene/gasoil mixture. Following the preparation of stable emulsions of precursor iron salt aqueous solution in the model oil, Fe2O3 nanoparticles have been synthesized at a typical reservoir temperature and high-enough pressure in an autoclave. Two series of core-flooding experiments have been performed by the injection of n-C6 and monitoring the hydrocarbon effective permeability before and after the damage. Two core plugs with the same flow zone index have been selected, one of which has been saturated with the model oil and the other with the emulsion, and the asphaltene damage has been induced by the core flooding test. The results of XRD and FESEM have indicated the crystalline structure and the mean particle size of 65 nm for the in-situ prepared nanoparticles, much smaller than the pore/throat sizes of the plugs. It is found out that the nanoparticles are effective in the control of the asphaltene damage at high injection/production flowrates. However, at low drawdown or injection/production flowrates, the in-situ synthesis procedure leads to a higher drop in the hydrocarbon effective permeability when compared to that observed during the displacement of the virgin model oil. Finally, this may be ascribed to the trapping of aqueous droplets in pore-throats, resulting in a significant reduction in the hydrocarbon permeability by the phase trapping.

In this research, 8-hydroxyquinoline was applied to synthesize FeQ3 nanoparticles and modification of Fe3O4 nanoparticles. Afterwards, synthesized FeQ3 and modified iron nanoparticles (HQ-Fe3O4) were used to prepare nanofiltration membranes. Therefore, by incorporation of different concentrations of nanoparticle as an additive into the PES as membrane matrix, PES/FeQ3 and PES/HQ-Fe3O4 nanofiltration membranes were constructed. FTIR analysis was used to nanoparticle and membrane characterization. Moreover, membranes morphology was evaluated by SEM and AFM analysis. The separation performance of prepared membranes was examined by pure water flux, MgSO4 rejection. The results showed the highest pure water flux (17.6 L/m2h) for M4 in 0.5 wt.% nanoparticles for both types of membranes. While it was 7.1 L/m2h for the pure membrane. Furthermore, the MgSO4 rejection increased to 91% in 0.5 wt.% of HQ-Fe3O4 nanoparticles, and it was 86% in 0.5 wt.% of Fe/Q3 nanoparticles. Moreover, the fouling resistance of fabricated membranes enhanced by incorporation these nanoparticles.

Nowadays, geomechanical studies in hydrocarbon reservoirs are interesting; therefore, the objective of this paper is to investigate the impact of facies, texture and diagenesis on the geomechanical behavior of the Asmari Formation. Three sets of data were used in this research: 1) sedimentological data including thin section petrography, porosity and density, 2) rock mechanical data comprising (1) uniaxial compressive strength, (2) cohesion, (3) friction angle, (4) tensile strength, and (5) brittleness and drillability indexes, and 3) ultrasonic data including S- and P-wave velocities. To investigate a possible relationship between the sedimentological features and rock mechanical behaviors, a facies index was defined based on the textural and diagenetic features allowing to quantify a sedimentary microfacies. Based on facies index, porosity and UCS five different geomechanical facies were determined for the Asmari Formation, where GMF1 to GMF5 represent very ductile to very brittle behavior respectively. Finally, the results of the study showed that mechanical behavior is controlled by the depositional and diagenetic overprints. Furthermore, by facies index, the rock strength may be predicted based on petrographical observations. Such studies are helpful for reducing the costs/time for acquiring geomechanical datasets and zoning mechanical stratigraphy.

Addition of nanoparticles to a polymeric matrix leads to enhance the performance of membrane gas separation. In this study, the aim is to find the optimum operative point of polymeric membrane modified by adding nanoparticles in gas separation. The assessed factors are type of nanoparticle, percentage of added nanoparticle, and cross membrane pressure.  Nanoparticles of AL2O3, ZnO, and TiO2 were used. Further, the ranges of nanoparticle concentration and operative cross membrane pressure were 2.5 to 15% and 2 to 25 bar respectively. To optimize a process, developing a robust model is necessary. Therefore, first, a powerful model based on artificial neural network was developed, which it was able to predict the values of permeability of oxygen, nitrogen, methane, and carbon dioxide. Neural network models were developed that had R2 greater than 0.9. Next, the optimum operative conditions for assessed gases were found using methodology based on genetic algorithm and considering four strategies. The results of optimization show that the maximum values of permeability for oxygen, nitrogen, methane, and carbon dioxide are 334.7, 779.9, 902.7, and 270.4 respectively.

Sand entering production wells is one of the oldest problems faced by oil companies and one of the toughest to solve. Production of sand during oil production causes severe operational problem for oil producers. Several techniques have been used for sand production control in sandstone reservoirs. Sand screens often serve as the primary barrier to sand production from weakly consolidated reservoirs. Screen technology has improved significantly in the areas of sand retention, mechanical integrity, and quality control. As future field developments trend towards more hostile environments, requiring fewer wells at higher rates, the overall reliability and longevity of sand screens will be increasingly more important in this high cost environment. Screening of IOR methods is a multi-criteria decision making process, and the Multi-Criteria Decision Making (MCDM) method as a systematic statistical method, can be applied in this regard. In this paper, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as one of the methods under the MCDM category was used to screen an Iranian oil well. The screening method was employed for 5 different sand control techniques using a wide range of properties and conditions. The analysis used a database of successful IOR projects across the Ahvaz and Mansuri fields. The relative importance of the reservoir parameters was determined based on the Analytic Hierarchy Process (AHP) at eight importance levels. The findings showed that Resin – Aromatic is the best method for that well then Slotted liner, ESS, Resin and Gravel Pack are in next ranks.