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Review Article

J. Energy Resour. Technol. 2018;141(3):030801-030801-18. doi:10.1115/1.4041096.

The oil production from any well passes through three stages. The first stage is the natural extraction of oil under the well pressure, the second stage starts when the well pressure decreases. This second stage includes flooding the well with water via pumping sea or brackish water to increase the well pressure and push the oil up enhancing the oil recovery. After the first and secondary stages of oil production from the well, 20–30% of the well reserve is extracted. The well is said to be depleted while more than 70% of the oil are left over. At this stage, the third stage starts and it is called the enhanced oil recovery (EOR) or tertiary recovery. Enhanced oil recovery is a technology deployed to recover most of our finite crude oil deposit. With constant increase in energy demands, EOR will go a long way in extracting crude oil reserve while achieving huge economic benefits. EOR involves thermal and/or nonthermal means of changing the properties of crude oil in reservoirs, such as density and viscosity that ensures improved oil displacement in the reservoir and consequently better recovery. Thermal EOR, which is the focus of this paper, is considered the dominant technique among all different methods of EOR. In this paper, we present a brief overview of EOR classification in terms of thermal and nonthermal methods. Furthermore, a comprehensive review of different thermal EOR methods is presented and discussed.

Commentary by Dr. Valentin Fuster

Research Papers: Energy Systems Analysis

J. Energy Resour. Technol. 2018;141(3):032001-032001-8. doi:10.1115/1.4041092.

In this paper, the hydrodynamic flow inside an internally circulating fluidized bed (ICFBG) was characterized using experimental and three-dimensional computational fluid dynamics (CFD) models. Eulerian-Eulerian model (EEM) incorporating the kinetic theory of granular flow was implemented in order to simulate the gas–solid flow. A full-scale plexiglass cold flow experimental model was built to verify simulation results prior to the fabrication of the gasifier. Six parameters were manipulated to achieve the optimum design geometry: fluidization flow rate of the draft tube (Qdt), aeration flow rate of the annulus (Qan), initial bed static height (Hbs), draft tube height (Hdt), draft tube diameter (Ddt), and orifice diameter (Dor). The investigated parameters showed strong effect on the particle flow characteristics in terms of the pressure difference (ΔP) and solid circulation rate (Gs). The predicted results by simulation for the optimum case were in close agreement with experimental measurements with about 5% deviation. The results show that the ICFBG operated stably with the maximum Gs value of 86.6 kg/h at Qdt of 350 LPM, Qan of 150 LPM, Hbs of 280 mm, Hdt of 320 mm, Ddt of 100 mm, and Dor of 20 mm.

Commentary by Dr. Valentin Fuster

Research Papers: Fuel Combustion

J. Energy Resour. Technol. 2018;141(3):032201-032201-6. doi:10.1115/1.4041106.

In the present study, a comprehensive mathematical method is developed to realize the flame expansion in the melting furnace zones. For this purpose, the furnace is composed of two zones: flame and post flame zones. Two different scenarios are covered in this research: Using lycopodium as a substitute fuel which is then converted to methane after the vaporization process, supplying the system with methane directly as a conventional fuel. The equations governing the problem with the required boundary conditions are developed and solved in each zone. The obtained results show great compatibility with the experimental findings in this research. Since lycopodium as the replacement fuel mostly contains volatile materials, one of the challenges in this study lies on understanding the effect of particle vaporization on the temperature distribution in a furnace. It is concluded that the average temperature in zones α1, α2, β1, and β2, is reduced by about 5 K, while it is increased by approximately the same amount in zones χ1, χ2, δ1, and δ2 after considering lycopodium as a fuel. Moreover, the role of vaporization and radiation on the combustion characteristics is studied in details. The achieved results from this analysis can be implemented in several industrial applications aiming for improving the energy efficiency outcome from their systems.

Commentary by Dr. Valentin Fuster
J. Energy Resour. Technol. 2018;141(3):032202-032202-8. doi:10.1115/1.4041095.

Biogas is a renewable source of energy produced by anaerobic digestion of organic material and composed mainly of methane (CH4) and carbon dioxide (CO2). Despite its lower heating value, biogas can still replace fossil fuels in several engineering stationary power generation and other industrial applications. Although numerous published studies were devoted to advance our understating of biogas combustion, experimental data of some parameters such as turbulent burning velocity (St) under certain operating conditions is still lacking. The present study aims to experimentally determine biogas turbulent burning velocity under normal temperature and pressure conditions. Turbulent premixed biogas–air flame was ignited at the center of a 29 L fan-stirred spherical combustion chamber of nearly homogeneous and isotropic turbulence. Test conditions consisted of varying turbulence intensity and biogas surrogate composition. Outwardly propagating biogas flames were tracked and imaged using Schlieren imaging technique. The results showed that, by increasing turbulence and reducing methane percentage in the surrogate, the flammability of the mixture shrinked. In addition, the curve fits of biogas turbulent burning velocity versus the equivalence ratio exhibited two different trends. The peak of turbulent burning velocity shifted away from nearly lean equivalence ratio toward the stoichiometric at a fixed turbulence intensity and higher CH4 percentage in the surrogate. However, for the same biogas surrogate composition, the peak of turbulent burning velocity shifted away from stoichiometric toward leaner equivalence ratio with increased turbulence intensity.

Commentary by Dr. Valentin Fuster

Research Papers: Petroleum Engineering

J. Energy Resour. Technol. 2018;141(3):032901-032901-11. doi:10.1115/1.4040073.

Compressor stations in natural gas networks should perform such that time-varying demands of customers are fulfilled while all of the system constraints are satisfied. Power consumption of compressor stations impose the most operational cost to a gas network so their optimal performance will lead to significant money saving. In this paper, the gas network transient optimization problem is addressed. The objective function is the sum of the compressor's power consumption that should be minimized where compressor speeds and the value status are decision variables. This objective function is nonlinear which is subjected to nonlinear and combinatorial constraints including both discrete and continuous variables. To handle this challenging optimization problem, a novel approach based on using two different structure intelligent algorithms, namely the particle swarm optimization (PSO) and cultural algorithm (CA), is utilized to find the optimum of the decision variables. This approach removes the necessity of finding an explicit expression for the power consumption of compressors as a function of decision variables as well as the calculation of objective function derivatives. The objective function and constraints are evaluated in the transient condition by a fully implicit finite difference numerical method. The proposed approach is applied on a real gas network where simulation results confirm its accuracy and efficiency.

Commentary by Dr. Valentin Fuster
J. Energy Resour. Technol. 2018;141(3):032902-032902-10. doi:10.1115/1.4041094.

In this work, an oil-soluble surfactant was studied to enhance crude oil mobilization in a cryolite-packed miniature bed. The cryolite packed bed provided a transparent, random porous medium for observation at the microscopic level. In the first part of the paper, oil-soluble surfactants, Span 80 and Eni-surfactant (ES), were dissolved directly into the crude oil. The porous medium was imbued with the crude oil (containing the surfactants), and de-ionized water was the flooding phase; in this experiment, the system containing ES had the best performance. Subsequently, sodium dodecyl sulfate (SDS), a hydrosoluble surfactant, was used to solubilize the ES, with the SDS acting as a carrier for the ES to the contaminated porous media. Finally, the SDS/ES micellar solutions were used in oil-removal tests on the packed bed. Grayscale image analysis was used to quantify the oil recovery effectiveness for the flooding experiments by measuring the white pixel percentage in the packed bed images. The SDS/ES flooding mixture had a better performance than the SDS alone.

Commentary by Dr. Valentin Fuster

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