Electron Injection Enhancement by Diamond-Like Carbon Film in Polymer Electroluminescence Devices

A diamond-like carbon （DLC） film is deposited as an electron injection layer between the polymer light-emitting layer（MEH-PPV） and aluminum （Al） cathode electrode in polymer electroluminescence devices （PLEDs） using a radio frequency plasma deposition system. The source material of the DLC is n-butylamine. The devices consist of indium tin oxide （ITO）/MEH-PPV/DLC/Al. Electron injection properties are investigated through I-V characteristics,and the mechanism of electron injection enhancement due to a thin DLC layer has been studied. It is found that： （1） a DLC layer thinner than 1.0nm leads to a higher turn-on voltage and decreased electroluminescent （EL） efficiency; （2） a 5.0nm DLC layer significantly enhances the electron injection and results in the lowest turn-on voltage and the highest EL efficiency; （3） DLC layer that exceeds 5.0nm results in poor device performance;and（4） EL emission can hardly be detected when the layer exceeds 10.0nm. The properties of ITO/MEH-PPV/DLC/Al and ITO/MEH-PPV/LiF/Al are investigated comparatively.

Development of Superhydrophobic Nano-SiO_(2)and Its Field Application in Low-permeability,High-temperature,and High-salinity Oil Reservoirs

In this study,to meet the stringent requirements on the hydrophobicity of nano-SiO_(2)particles for use in depressurization and enhanced injection operations in high-temperature and high-salinity oil reservoirs,secondary chemical grafting modification of nano-SiO_(2)is performed using a silane coupling agent to prepare superhydrophobic nano-SiO_(2) particles.Using these superhydrophobic nano-SiO_(2)particles as the core agent,and liquid paraffin or diesel as the dispersion medium,a uniform dispersion of nano-SiO_(2)particles is achieved under high-speed stirring,and a chemically enhanced water injection system with colloidal stability that can be maintained for more than 60 d is successfully developed.Using this system,a field test of depressurization and enhanced injection is carried out on six wells in an oilfield,and the daily oil production level is increased by 11 t.The cumulative increased water injection is 58784 m^(3),the effective rate of the measures was 100%,and the average validity period is 661 d.

Injection of biosurfactant and chemical surfactant following hot water injection to enhance heavy oil recovery

This study investigates the potential of enhancing oil recovery from a Middle East heavy oil field via hot water injection followed by injection of a chemical surfactant and/or a biosurfactant produced by a Bacillus subtilis strain which was isolated from oil-contaminated soil.The results reveal that the biosurfactant and the chemical surfactant reduced the residual oil saturation after a hot water flood.Moreover,it was found that the performance of the biosurfactant increased by mixing it with the chemical surfactant.It is expected that the structure of the biosurfactant used in this study was changed when mixed with the chemical surfactant as a probable synergetic effect of biosurfactant-chemical surfactants was observed on enhancing oil recovery,when used as a mixture,rather than alone.This work proved that it is more feasible to inject the biosurfactant as a blend with the chemical surfactant,at the tertiary recovery stage.This might be attributed to the fact that in the secondary mode,improvement of the macroscopic sweep efficiency is important,whereas in the tertiary recovery mode,the microscopic sweep efficiency matters mainly and it is improved by the biosurfactantchemical surfactant mixture.Also as evidenced by this study,the biosurfactant worked better than the chemical surfactant in reducing the residual heavy oil saturation after a hot water flood.

Application of supervised machine learning to predict the enhanced gas recovery by CO_(2) injection in shale gas reservoirs

The technique of Enhanced Gas Recovery by CO_(2) injection(CO_(2)-EGR)into shale reservoirs has brought increasing attention in the recent decade.CO_(2)-EGR is a complex geophysical process that is controlled by several parameters of shale properties and engineering design.Nevertheless,more challenges arise when simulating and predicting CO_(2)/CH4 displacement within the complex pore systems of shales.Therefore,the petroleum industry is in need of developing a cost-effective tool/approach to evaluate the potential of applying CO_(2) injection to shale reservoirs.In recent years,machine learning applications have gained enormous interest due to their high-speed performance in handling complex data and efficiently solving practical problems.Thus,this work proposes a solution by developing a supervised machine learning(ML)based model to preliminary evaluate CO_(2)-EGR efficiency.Data used for this work was drawn across a wide range of simulation sensitivity studies and experimental investigations.In this work,linear regression and artificial neural networks(ANNs)implementations were considered for predicting the incremental enhanced CH4.Based on the model performance in training and validation sets,our accuracy comparison showed that(ANNs)algorithms gave 15%higher accuracy in predicting the enhanced CH4 compared to the linear regression model.To ensure the model is more generalizable,the size of hidden layers of ANNs was adjusted to improve the generalization ability of ANNs model.Among ANNs models presented,ANNs of 100 hidden layer size gave the best predictive performance with the coefficient of determination(R2)of 0.78 compared to the linear regression model with R2 of 0.68.Our developed MLbased model presents a powerful,reliable and cost-effective tool which can accurately predict the incremental enhanced CH4 by CO_(2) injection in shale gas reservoirs.

SLIP VELOCITY MODEL OF POROUS WALLS ABSORBED BY HYDROPHOBIC NANOPARTICLES SIO_2

According to new slip effects on nanopatterned interfaces, the mechanism of enhancing water injection into hydrophobic nanomaterial SiO2 was proposed. When Hydrophobic Nanoparticles（HNPs）are adsorbed on surfaces of porous walls, hydrophobic nanoparticles layers are formed instead of hydrated layer, and slip effects appear on the pore wall when a driving pressure is applied to the rock cores sample. It makes fluid to move more quickly and the flow capacity increases greatly. Experiments on changing wettability of porous walls were conducted, and the phenomenon that porous walls surfaces were adsorbed by nanoparticles was validated with the Environment Scan Electron Microscopy（ESEM）. The results of displacement experiments show that flowing resistance is greatly reduced, and water-phase effective permeability is increased by 47 % averagely after being treated by nanofluid. These results indicate that the slip effect may occur on nanoparticle film of porous walls. Based on this new mechanism of enhancing water injection about hydrophobic nanomaterial SiO2, a slip velocity model in uniform porous media was introduced, and some formulas for the ratio of slip length to radius, slip length ,stream slip velocity and flux increment were deduced. and calculated results indicate that the ratio of slip length to radius is about 3.54%-6.97%, and the slip length is about 0.024 μ m -0.063 μ m. The proposed model can give a good interpretation for the mechanisms of enhancing water injection with the HNPs.