Screening of enhanced oil recovery (EOR) methods is a main prerequisite for EOR planning and design. In this study, an integrated data‐driven screening model (DDSM) is developed to improve EOR screening using the combined capabilities of the fuzzy expert approach (FEA) and support vector regression (SVR) techniques. In this study, EOR field data from the past 40 years were reviewed to generate an updated and reliable EOR criteria table as a basis to construct a fuzzy screening model. The DDSM was evaluated to determine the quantitative screening and ranking of EOR methods using seven field datasets, including the fast forecasting of the nominated EOR methods. In order to improve screening performance, a fuzzy model was integrated using 4 SVR models to predict the adaptive weights of the screening parameter for decision making. The SVR models can predict the recovery factor (RF) of EOR methods including gas, chemical, steam, and combustion to calculate the adaptive effective weight of the screening parameters. The SVR models were trained with datasets generated from simulations of the EOR process. The absolute average error (AAE) of the SVR models from the simulation varied within the range of 0.078–0.095 for the prediction of the RF. The DDSM results were compatible to the data published in other literature. In addition, the developed model can provide comparable results to common screening software. The results showed improvements due to the adaptive weighting system on the EOR methods’ screening for the studied reservoirs relative to the fuzzy engine with constant weights. The presented integrated model can guide the screening process to select the efficient EOR method in practical applications.

The Brownian motion of the nanoparticles in nanofluid is one of the potential contributors to enhance effective thermal conductivity and the mechanisms that might contribute to this enhancement are the subject of considerable discussion and debate. In this paper, the mixing effect of the base fluid in the immediate vicinity of the nanoparticles caused by the Brownian motion was analyzed, modeled, and compared with existing experimental data available in the literature. CFD was developed to study the effect of wall/nanoparticle interaction on forced convective heat transfer in a tube under constant wall temperature condition. The results showed that the motion of the particle near the wall which can decrease boundary layer and the hydrodynamics effects associated with the Brownian motion have a significant effect on the convection heat transfer of nanofluid.

In this paper, computational fluid dynamic modelling was developed to study the effect of the floating jet velocity or submerged rotary jet in sludge prevention in a large crude-oil storage tank. The Euler-Euler method was used in a two-dimensional CFD model to describe oil and sludge flow behaviour at the bottom of the storage tank. By modifying some parameters, the k-e model was used to describe the turbulence of the mixing flow. The results show the effect of jet velocity, angle, and time on the mixing process. By increasing the velocity from 5 m s −1 , the mixing pattern significantly changes and improves the mixing of the sludge with crude oil. To evaluate the results, chosen was the sludge profile related to the bottom of the sample tank, and modelling results showed an 80 cm reduction in thickness of the sludge, which corresponds well to the profile of the bottom of the tank. In addition, the y + axis indicated that the amounts at all points were less than 300, which is acceptable in two-phase modelling. A. A. LOTFI NEYESTANAK et al.: Study on the Effect of Jet Velocity on Mixing Performance..., (2017) 229−239 230 tern in a tank equipped with a jet mixer. Jayanti 11 studied the recycling flow pattern in jet mixers by applying computational fluid dynamics (CFD) and coding, and found that flat, elliptical, half-circle and cone bottom tanks are more practical for the mixing process. Studies have shown that the circulation flow pattern is considerably dependent on the tank shape, whereas mixing time is significantly dependent on the circulation flow pattern. A. W. Patwardhan 12 compared experimental data with modelling results, and found that mixing time is a function of nozzle diameter and jet inclination angle, moreover, showed that the nozzle diameter had an inverse relation to the jet inclination angle, and direct relation to power. Zughbi 13 performed research on the effect of jet height and angle by applying CFD technique in addition to the effect of jet angle on mixing time. For a side-entry jet, and with two conditions; ratio of height to diameter of 1 and maximum length of the jet corresponding to the injection angle of 45°, the minimum mixing time was not achieved, but was obtained at the angle of 30°. The end of the mixing process is defined based on the existing steady state of a component composition at a specific point. Keywords Submerged rotary jet mixer, large-scale oil storage tank, sludge prevention, modelling of fluid flow, computational fluid dynamics, Euler-Euler method Experimental Numerical modellingThe first step in multiphase problems leads to choosing the equations, and proper solution is determining the flow pattern. The two main approaches are Euler and Lagrange methods, but because the volume fraction in these two approaches is not negligible, the Euler-Euler model and Euler method have been used in the present study. The Eulerian model theory is the most complicated model for multiphase systems. In this model, the types of continuity and momentum equations have ...

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