Synthesis and characterization of low cost willemite based glass–ceramic for opto-electronic applications

This paper presents a comprehensive study on thermal, structural and optical properties of novel willemite glass–ceramics. The precursor glass in the ZnO–SLS glass system was successfully prepared using conventional melt-quenching technique and willemite (Zn₂SiO₄) glass–ceramics were derived from this precursor glass by a control crystallization process. The effect of heat-treatment temperature on the phase transformation, morphology and size of Zn₂SiO₄ crystal phase was examined using X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM) techniques. Furthermore, fourier transform infrared reflection (FTIR) spectroscopy was used to evaluate the Zn₂SiO₄ crystal structural evolution. The average size of Zn₂SiO₄ crystallite obtained from calculation of XRD is found to be in the range 30–60 nm, whereas the grain size observed in FESEM is in range of 200–400 nm. The appearance of SiO₂, ZnO₄ and Zn–O–Si bands detected from FTIR indicate the formation of Zn₂SiO₄ crystal phase. Besides, the study of the optical band gap has found that optical band gap of the glass–ceramics decreased as the heat treatment temperature increased. The photoluminescence spectra of willemite glass–ceramics exhibit two different emissions around 525 nm (green) and 585 nm (yellow); exhibit a characteristic of broad absorption band around 260 nm. These two different spectra reveal that the luminescence performance of the willemite glass–ceramics was enhanced with the progression of heat treatment temperature due to different located energy levels of the β-Zn₂SiO₄ and α-Zn₂SiO₄ crystalline phase. Such luminescent glass–ceramics was expected to find potential applications in phosphors and opto-electronic devices.     

Evaluating the Unloading Gradient Pressure in Continuous Gas-lift Systems During Petroleum Production Operations

Evaluating the performance, applicability, and field testing of various artificial lift methods, in particular continued gas-lift, can be time consuming and costly. To overcome these drawbacks, it is needed to propose a reliable model to estimate gas-lift applicability in advance of the installation under specific well operational conditions such as tubing size and design oil rate. In this study, the robust least square modification of support vector machine (LSSVM) methodology is implemented to propose a computer program, by which the unloading pressure gradient region can be determined in various design oil production rates and also tubing sizes. The developed LSSVM model results indicate 1.084% average absolute relative deviation from the corresponding unloading pressure gradient literature values, and squared correlation coefficient of 0.9994.     

An Experimental Phase Diagram of a Gas Condensate Reservoir

Abstract

Most condensate gas reservoirs discovered in Iran are rich in intermediate components. The phase diagram of these fluids is essential for reservoir management. The available commercial reservoir fluid simulators are not able to find the critical point of some of these condensate gas reservoirs to obtain the phase diagram quality lines. The quality lines of these reservoir's samples can be obtained experimentally using the constant mass expansion test data. The authors present experimental development of gas condensate phase diagram along with retrograde condensation and revaporization characterization during the reservoir depletion process of one Iranian condensate gas reservoir.     

On the determination of breakthrough time for water coning phenomenon in oil reservoirs

The water coning phenomenon leads to decrease the wellhead pressure with moving of water into oil production zone, which is regarded as one of most serious problems during oil production. Therefore, the development of reliable models is important to predict the water coning breakthrough time, and consequently avoid the water coning phenomenon and production of water. To this end, the artificial neural network modeling strategy optimized with particle swarm optimization, least square support vector machine (LSSVM) approach coupled with the coupled simulated annealing optimization method, and finally decision tree method are implemented in current study to accurately predict the dimensionless breakthrough time of water coning. The results obtained in the present study demonstrate that the models proposed provide acceptable results in predicting the dimensionless breakthrough time of water coning. Furthermore, comparative study conducted illustrates the superiority of LSSVM methodology in terms of accuracy compared to the other methods investigated.     

Sorption of acid dyes onto GLA and H2SO4 cross-linked chitosan beads

The feasibility of chitosan and cross-linked chitosan beads as efficient adsorbents for the removal of Acid Blue 25 (AB 25) and Acid Red 37 (AR 37) from aqueous solution was studied by using batch adsorption techniques. Glutaraldehyde (GLA) and sulphuric acid were employed as cross-linking reagents. The effect of process parameters like pH, agitation period, agitation rate and concentrations of dyes on the extent of AR 37 and AB 25 adsorption by chitosan and cross-linked chitosan beads was investigated. The best interpretation for the equilibrium data was given by the Langmuir isotherm, while the pseudo-second-order kinetic model could best describe the adsorption of these acid dyes. Thermodynamic parameters such as enthalpy change (ΔH°), free energy change (ΔG°) and entropy change (ΔS°) were estimated and adsorption process was spontaneous and exothermic. The desorbed chitosan, chitosan-GLA and chitosan-H₂SO₄ beads can be reused to adsorb the acid dyes. Results also showed that chitosan, chitosan-GLA and chitosan-H₂SO₄ beads were favourable adsorbers.     

Shorea dasyphylla sawdust for humic acid sorption

The efficacy of Shorea dasyphylla sawdust as an adsorbent for the removal of humic acid from aqueous solution was investigated as a function of pH value, agitation period, agitation rate, initial humic acid concentration and adsorbent dosage. The equilibrium nature of humic acid adsorption was described by the Langmuir, Freundlich and BET isotherms. The experimental adsorption data was best fitted to the Langmuir adsorption model, which gave adsorption capacity of 68.4 mg humic acid adsorbed per gram Shorea dasyphylla sawdust at pH 2.0 and initial humic acid concentration of 80 mg?L^-1. Kinetic studies indicated that the sorption process followed the pseudo-second-order kinetic model. It was revealed that after three cycles of adsorption and desorption, Shorea dasyphylla sawdust retained its promising adsorption ability. With an initial amount of 73.5 mg humic acid adsorbed per gram sawdust, more than 80% of humic acid desorbed by using 0.1 M HCl. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were employed to study the mechanism of the removal of humic acid. From the dimensionless factor, R_L data, it was determined that the adsorption of humic acid onto untreated Shorea dasyphylla sawdust was favourable.     

Structural and Optical Properties of Lead-Boro-Tellurrite Glasses Induced by Gamma-Ray

Spectrophotometric studies of lead borotellurite glasses were carried out before and after gamma irradiation exposure. The increasing peak on the TeO₄ bi-pyramidal arrangement and TeO₃₊₁ (or distorted TeO₄) is due to augmentation of irradiation dose which is attributed to an increase in degree of disorder of the amorphous phase. The structures of lead tellurate contain Pb₃TeO₆ consisting of TeO₃ trigonal pyramid connected by PbO₄ tetragonal forming a three-dimensional network. The decrease of glass rigidity is due to irradiation process which is supported by the XRD diffractograms results. The decreasing values of absorption edge indicate that red shift effect occur after irradiation processes. A shift in the optical absorption edge attributed to an increase of the conjugation length. The values of optical band gap, E_opt were calculated and found to be dependent on the glass composition and radiation exposure. Generally, an increase and decrease in Urbach’s energy can be considered as being due to an increase in defects within glass network.     

Efficient screening of enhanced oil recovery methods and predictive economic analysis

Oil demand for economic development around the world is rapidly increasing. Moreover, oil production rates are getting a peak in mature reservoirs and tending to decline in the near future, which has led to considerable researches on enhanced oil recovery (EOR) methods. Therefore, an efficient technical and economical screening to appropriate selection of EOR methods can make savings in time and cost. The purpose of this communication is to present a method to select an efficient EOR process and investigate its economic parameters. A database of reservoir parameters of rock and fluid properties along with successful EOR techniques has been collected and analyzed. First, an artificial neural network (ANN) was developed to classify the EOR methods technically. Then, an economical EOR screening model was designed, and then, future cash flows on the use of EOR methods were predicted. The results show that the ANN system can select proper EOR methods and classify them. Moreover, the obtained results indicate that the economic analysis performed in this study is efficient and useful to predict future cash flows.     

Data-driven modeling of heavy oil viscosity in the reservoir from geophysical well logs

Viscosity is the most crucial fluid property on recovery and productivity of hydrocarbon reservoirs, more particularly heavy oil reservoirs. In heavy and extra heavy oil reservoirs e.g. bitumen and tar sands more energy is required to be injected into the system in order to decrease the viscosity to make the flow easier. Therefore, attempt to develop a reliable and rapid method for accurate estimation of heavy oil viscosity is inevitable. In this study, a predictive model for estimating of heavy oil viscosity is proposed, utilizing geophysical well logs data including gamma ray, neutron porosity, density porosity, resistivity logs, spontaneous potential as well as P-wave velocity and S-wave velocity and their ratio (Vp/Vs). To this end, a supervised machine learning algorithm, namely least square support vector machine (LSSVM), has been employed for modeling, and a dataset was provided from well logs data in a Canadian heavy oil reservoir, the Athabasca North area. The results indicate that the predicted viscosity values are in agreement with the actual data with correlation coefficient (R²) of 0.84. Furthermore, the outlier detection analysis conducted shows that only one data point is out of the applicability of domain of the develop model.     

Characterization of C7+ fraction properties of crude oils and gas-condensates using data driven models

Petroleum fractions, in particular heptane-plus fraction (C₇₊), are complex mixtures and measuring properties of hydrocarbon-plus fractions is difficult and time-consuming. In this study, the normal boiling point, specific gravity and molecular weight of C₇₊ are estimated as a function of molecular weight, specific gravity as well as cumulative weight fractions using four soft computing strategies called least squares support vector machine, decision tree, gene expression programing, and artificial neural network. The results obtained in this study demonstrate that the developed models could be applied properly for the characterization and estimation of hydrocarbon–plus properties of crude oils and gas-condensates.