Nanoparticles (NPs) have unique capabilities that make them an eye-opener opportunity for the upstream oil industry. Their nano-size allows them to flow within reservoir rocks without the fear of retention between micro-sized pores. Incorporating NPs with drilling and completion fluids has proved to be an effective additive that improves various properties such as mud rheology, filtration, thermal conductivity, and wellbore stability. However, the biodegradability of drilling fluid chemicals is becoming a global issue as the discharged wetted cuttings raise toxicity concerns and environmental hazards. Therefore, it is urged to utilize chemicals that tend to break down and susceptible to biodegradation. This research presents the practical application of bio-based Zinc Oxide nanoparticles (ZnO NPs) prepared chemically from celery leaf plant extract as green additive in water-based mud drilling fluid (WBM). The study aimed to evaluate the filtration and thermal stability of WBM using green-synthesized ZnO NPs. The results showed that the ZnO NPs have minimal effect of mud density, but significant improvement in mud thermal stability and filtration properties were attained with concentrations lower than 1g. The fluid loss rate was reduced by 33% with 0.45g of ZnO nanoparticles, and the thinnest mud cake was obtained as well. In terms of thermal stability, the bio-based ZnO NPs greatly enhanced the rheological properties of WBM at elevated temperatures. The rate of increment in plastic viscosity (PV) or decrement in yield point (YP) and gel strength occurred in a controllable manner compared to the rheological properties of base mud at high temperatures reaching 90°C. This study provides insight into the effect of green-synthesized ZnO nanoparticles on the performance of water-based mud and highlights their potential as an effective and environmentally friendly additive for the oil and gas industry.
The sensitivity of SnO2 nanoparticles/reduced graphene oxide hybrid to NO2 gas is discussed in the present work using density functional theory (DFT). The SnO2 nanoparticles shapes are taken as pyramids, as proved by experiments. The reduced graphene oxide (rGO) edges have oxygen or oxygen-containing functional groups. However, the upper and lower surfaces of rGO are clean, as expected from the oxide reduction procedure. Results show that SnO2 particles are connected at the edges of rGO, making a p-n heterojunction with a reduced agglomeration of SnO2 particles and high gas sensitivity. The DFT results are in
Well-dispersed Cu2FeSnSe4 (CFTSe) nanoparticles were first synthesized using the hot-injection method. The structure and phase purity of as-synthesized CFTSe nanoparticles were examined by X-ray diffraction (XRD) and Raman spectroscopy. Their morphological properties were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The average particle sizes of the nanoparticles were about 7-10 nm. The band gap of the as-synthesized CFTS nanoparticles was determined to be about 1.15 eV by ultraviolet-visible (UV-Vis) spectrophotometry. Photoelectrochemical characteristics of CFTSe nanoparticles were also studied, which indicated their potential application in solar energy water splitting.
Experimental tests were conducted to investigate the thermal performance (cooling effect) of water mist system consisting of 5μm volume median diameter droplets in reducing the heat gain entering a room through the roof and the west wall by reducing the outside surface temperature due to the evaporative cooling effect during the hot dry summer of Baghdad/Iraq. The test period
was Fifty one days during the months May, June, and July 2012. The single test day consists of 16 test hours starting from 8:00 am to 12:00 pm. The results showed a reduction range of 1.71 to 15.5℃ of the roof outside surface temperature and 21.3 to 76.6% reduction in the daily heat flux entering the room through the roof compared with the case of not using w
In This paper, CuO thin films having different thickness (250, 300 , 350 and 400) nm were deposited on glass substrates by thermal vacuum evaporator. The thermal oxidation of this evaporated film was done in heated glass at temperature (300 in air at one hour. The study of X-ray diffraction investigated all the exhibit polycrystalline nature with monoclinic crystal structure include uniformly grains. Thin film’s internal structure topographical and optical properties. Furthermore, the crystallization directions of CuO (35.54 , 38.70 ) can be clearly observed through an X-ray diffraction analysis XRD, Atomic Force Microscope AFM (topographic image) showed that the surface Characteristics , thin films crystals grew with increases in either
... Show MoreIn the present work, asphaltenes and resins separated from emulsion samples collected from two Iraqi oil wells, Nafut Kana (Nk) and Basrah were used to study the emulsion stability. The effect of oil resins to asphaltene (R/A) ratio, pH of the aqueous phase, addition of paraffinic solvent (n-heptane), aromatic solvent (toluene), and blend of both (heptol) in various proportions on the stability of emulsions had been investigated. The conditions of experiments were specified as an agitation speed of 1000 rpm for 30 minutes, heating at 50 °C, and water content of 30%. The results showed that as the R/A ratio increases, the emulsion will be unstable and the amount of water separated from emulsion increases. It was noticed that the em
... Show MoreIn this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and
... Show MoreThis research deals with increasing the hardening and insulating the petroleum pipes against the conditions and erosion of different environments. So, basic material of epoxy has been mixed with Ceramic Nano Zirconia reinforcement material 35 nm with the percentages (0,1,2,3,4,5) %, whereas the paint basis of broken petroleum pipes was used to paint on it, then it was cut into dimensions (2 cm. × 2 cm.) and 0.3cm high. After the paint and percentages are completed, the samples were immersed into the paint. Then, the micro-hardness was checked according to Vickers method and thermal inspection of paint, which contained (Thermal conduction, thermal flux and Thermal diffusivity), the density of the painted samples was calculate
... Show MoreAbstract
Hexapod robot is a flexible mechanical robot with six legs. It has the ability to walk over terrain. The hexapod robot look likes the insect so it has the same gaits. These gaits are tripod, wave and ripple gaits. Hexapod robot needs to stay statically stable at all the times during each gait in order not to fall with three or more legs continuously contacts with the ground. The safety static stability walking is called (the stability margin). In this paper, the forward and inverse kinematics are derived for each hexapod’s leg in order to simulate the hexapod robot model walking using MATLAB R2010a for all gaits and the geometry in order to derive the equations of the sub-constraint workspaces for each
... Show More