n-Decane hydro-conversion over bi- and tri-metallic Al-HMS catalyst in a mini-reactor

Bi-metallic(Pt–Sn and Sn–Ni) and tri-metallic(Pt–Sn–Ni) catalysts,supported on Al-containing hexagonal mesoporous silica(Al-HMS)(Si/Al = 20) materials,were synthesized.N_2 adsorption–desorption,X-ray diffraction(XRD),Brunauer–Emmett–Teller(BET) test,and temperature programed desorption(NH3-TPD)were used to characterize physicochemical characteristics and textural properties of the Al-HMS catalysts.Catalytic performances on hydro-cracking of n-decane at different reaction conditions were studied in a microreactor.Comparison between Pt–Sn,Sn–Ni and Pt–Sn–Ni catalyst under different hydro-cracking conditions was made.The experimental results indicate that the proper balance between the acid and metal functions is the key in synthesizing a catalyst with a better performance in hydro-cracking.Tri-metallic catalyst exhibits the best catalytic performance in n-decane hydro-cracking than two bi-metallic catalysts.     

A developed smart technique to predict minimum miscible pressure—eor implications

Miscible gas injection (MGI) processes such as miscible CO₂ flooding have been in use as attractive EOR options, especially in conventional oil reserves. Optimal design of MGI is strongly dependent on parameters such as gas–oil minimum miscibility pressure (MMP), which is normally determined through expensive and time‐consuming laboratory tests. Thus, developing a fast and reliable technique to predict gas–oil MMP is inevitable. To address this issue, a smart model is developed in this paper to forecast gas–oil MMP on the basis of a feed‐forward artificial neural network (FF‐ANN) combined with particle swarm optimisation (PSO). The MMP of a reservoir fluid was considered as a function of reservoir temperature and the compositions of oil and injected gas in the proposed model. Results of this study indicate that reservoir temperature among the input parameters selected for the PSO–ANN has the greatest impact on MMP value. The developed PSO–ANN model was examined using experimental data, and a reasonable match was attained showing a good potential for the proposed predictive tools in estimation of gas–oil MMP. Compared with other available methods, the proposed model is capable of forecasting oil–gas MMP more accurately in wide ranges of thermodynamic and process conditions. All predictive models used other than the PSO–ANN model failed in providing a good estimate of the oil–gas MMP of the hydrocarbon mixtures in Azadegan oilfield, Iran. © 2013 Canadian Society for Chemical Engineering     

Investigation of Drill Cuttings Reinjection: Environmental Management in Iranian Ahwaz Oilfield

Abstract

This study was to assess the feasibility of drill cuttings reinjection (DCRI) by hydraulic fracturing in two typical wellbore configurations: annulus injection and a dedicated disposal well by tubing and packer completion in one of the oil wells in Ahwaz oilfield. This article shows that the Mishan formation is more appropriate for DCRI in the Ahwaz oilfield. The optimum parameters such as injection rate, batch size, and shut-in time were simulated and used to determine the geometry of hydraulic fractures created during injection and to ensure that the fractures are precisely developed within the targeted injection zone.     

Investigating Geochemical Characterization of Asmari and Bangestan Reservoir Oils and the Source of H2S in the Marun Oilfield

Abstract

Geochemical evaluation is one of the most important and applicable methods for optimization of hydrocarbon exploration and production. In this article, the geochemistry of Asmari and Bangestan reservoir oils of Marun oil field was experimentally studied. Marun oil field is one of the giant oil fields in southwest Iran and has two oil reservoirs (Asmari and Bangestan) and one gas reservoir (Khami). The main goal of this study is to investigate the genetic behavior of the above oil reservoirs, focusing mainly on hydrogen sulfide pollutants. Biomarkers of saturated and aromatic fractions were studied on five polluted, three unpolluted Asmari, and two Bangestan reservoir oils. A triangular diagram was used to determine the chemical composition of the studied oil. The results show relatively higher oil maturity for both reservoirs with no biodegradation. The carbon preference index of both reservoir oils was also around 1, which indicates mature oil samples. The pristane-to-phytane ratio, Pri/nC₁₇ versus Phy/nC₁₈, terrigenous/aquatic ratio (TAR), and geochemical data all show that the source rock for both Asmari and Bangestan reservoirs is the same. This source rock was deposited in a reducing environment with algae (kerogen type II) organic matter and without any higher plants. Genetic potential studies of probable source rocks, by means of Rock-Eval VI analysis in Marun oilfield, present Kazhdomi and Garu as main source rocks. Biomarkers of sulfur compounds and structural analysis of Marun oil field revealed that hydrogen sulfide gas pollution in the Asmari reservoir originated from the Bangestan reservoir. In addition, thermal sulfate reduction was a possible main process for hydrogen sulfide formation.     

The Influence of Multiphase Flow on the Interpretation of Pressure Transient Data in Multilayer Reservoirs With Cross Flow

The authors investigated multiphase flow effects on the analysis of pressure responses in layered reservoirs with cross flow. Virtually all studies on the subject of multiphase well test analysis have been carried out in single-layer reservoirs. However, many reservoirs are found to be composed of number of layers whose characteristics are different from each other and the wells in such reservoirs may be completed and produced from more than one layer. In order to examine the behavior of pressure data in multiphase multilayer reservoirs with interlayer communication several cases are considered. The contrast in phase saturations in each layer is the parameter of interest. For each case the applicability of conventional well test analysis is investigated and reservoir parameters such as phase mobilities, skin factor, and average reservoir pressure are compared with actual values. According to the results of this work, conventional multiphase well test analysis in the single layer reservoir can be used in layered reservoirs with cross flow; however, the data should be interpreted with care if horizontal saturation gradient in the layers is significant.     

Estimating phase behavior of the asphaltene precipitation by GA-ANFIS approach

Abstract

This study implements an adaptive neuro-fuzzy inference system (ANFIS) approach to predict the precipitation amount of the asphaltene using temperature (T), dilution ratio (R_v), and molecular weight of different n-alkanes. Results are then evaluated using graphical and statistical error analysis methods, confirming the model’s great ability for appropriate prediction of the precipitation amount. Mean squared error and determination coefficient (R²) values of 0.036 and 0.995, respectively are obtained for the proposed ANFIS model. Results are then compared to those from previously reported correlations revealing the better performance of the proposed model.     

Experimental investigation and modeling hybrid nano-porous membrane process for industrial oily wastewater treatment

The aims of this work are to construct a pilot scale purification set-up using membrane process for Tehran Oil Refining Company desalter plant wastewater. The investigation was shown that the high amount of impurities in the feed was the main reason of low permeation flux. The nano-porous membrane-powdered activated carbon (NPM–PAC) was employed to settle this problem. Results demonstrated NPM alone was ineffective in removing TSS, COD, and TOC. In the NPM process the removal of COD and TOC are around 62.5 and 75.1%, respectively, and the steady permeation flux (SPF) is around 78.7 L/(m² h). Optimum PAC dosage, which leads to less deposit layer with high porosity on the membrane surface, could increase permeation flux up to 133.8 L/(m² h), the removal of COD and TOC, 78.1% and 90.4%, respectively, and also decreased steady fouling resistance (SFR) around 46.1%. Hermia's models were employed to investigate mechanism of preventing membrane fouling. After coagulation, the kinetic constants, K_b, K_i, K_s, and K_c, showed lower amounts when NPM filtration used alone. Thus, a NPM–PAC hybrid membrane system has the potential to be an effective method to improve NPM removal efficiency in high percentages as well as to improve membrane fouling and permeation flux in desalter plant.     

Investigation of different precipitating agents effects on performance of γ-Al2O3 nanocatalysts for methanol dehydration to dimethyl ether

This research includes synthesis of nano-sized γ-Al₂O₃ catalyst for methanol dehydration reaction to produce DME. A co-precipitation method with four precipitants including sodium carbonate, sodium bicarbonate, ammonium carbonate and ammonium bicarbonate was used for this purpose. The catalysts were characterized by XRD, FTIR, NH₃-TPD, SEM, TEM and N₂ adsorption–desorption techniques. In order to investigate the catalyst activity, they were used in the fixed bed micro reactor for methanol dehydration reaction at different operating conditions. Also, a commercial γ-Al₂O₃ nanocatalyst was utilized for comparison purposes. The catalyst prepared by ammonium carbonate showed the highest yield of DME in comparison with other ones.     

Estimation of water content of natural gases using particle swarm optimization method

A precise estimation of natural gas water content is a significant constraint in appropriate planning of natural gas production, processing services and transmission. The main contribution of this research is to develop a machine learning approach for predicting water content of sweet and sour natural gases. In this regard, a joining of particle swarm optimization and an artificial neural network was utilized. The suggested model presents good predictions of the sour natural gas water content with following circumstances, including CO₂ contents of 0–40 mol%, H₂S contents of 0–50 mol%, pressures in range from atmospheric to 70,000 KPa for sour gas and 100,000 KPa for sweet gas, and temperatures from 10–200°C for sweet gases and 10–150°C for sour gases.