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| 1 | How much would silica nanoparticles enhance the performance of low-salinity water flooding?显示文摘Nanofluids and low-salinity water(LSW)flooding are two novel techniques for enhanced oil recovery.Despite some efforts on investigating benefits of each method,the pros and cons of their combined application need to be evaluated.This work sheds light on performance of LSW augmented with nanoparticles through examining wettability alteration and the amount of incremental oil recovery during the displacement process.To this end,nanofluids were prepared by dispersing silica nanoparticles(0.1 wt%,0.25 wt%,0.5 wt% and 0.75 wt%)in 2,10,20 and 100 times diluted samples of Persian Gulf seawater.Contact angle measurements revealed a crucial role of temperature,where no wettability alteration occurred up to 80 ℃.Also,an optimum wettability state(with contact angle 22°)was detected with a 20 times diluted sample of seawater augmented with 0.25 wt% silica nanoparticles.Also,extreme dilution(herein 100 times)will be of no significance.Throughout micromodel flooding,it was found that in an oil-wet condition,a combination of silica nanoparticles dispersed in 20 times diluted brine had the highest displacement efficiency compared to silica nanofluids prepared with deionized water.Finally,by comparing oil recoveries in both water-and oil-wet micromodels,it was concluded that nanoparticles could enhance applicability of LSW via strengthening wettability alteration toward a favorable state and improving the sweep efficiency. | Amir Hossein Saeedi Dehaghani Reza Daneshfar | 2019 | Petroleum Science2019，16，3： | 2 |
| 2 | On the prediction of filtration volume of drilling fluids containing different types of nanoparticles by ELM and PSO-LSSVM based models显示文摘There is a direct link between the extent of formation damage and the filtration volume of the drilling fluids in hydrocarbon reservoirs.The filtration volume can be diminished by adding different additives to the drilling fluids.Recently,nanoparticles have been extensively used for enhancing the filtration characteristics of the drilling fluids.However,there is no reliable model for investigating the influence of this class of additives on the performance of drilling fluids.Hence in this study,two powerful tools ELM(extreme learning machine)and PSO-LSSVM(particle swarm optimization-least square support vector machine)are applied to determine the effect of various nanoparticles on the filtration volume.The assessment of the models is carried out by computing the statistical parameters,and it is found that ELM has a greater ability to predict the filtration volumes,while PSO-LSSVM performs satisfactorily too.The model predictions and experimental results are in excellent agreement as suggested by the values of root mean squared error(RMSE=0.2459),coefficient of determination(R^(2)=0.999),and mean relative error(MRE=2.028%)for the dataset.The statistical analysis shows that the suggested model can predict the filtration volume with great accuracy.Moreover,through sensitivity analysis of the input parameters,it is found that for a specified nanoparticle,the filtration volume is highly influenced by nanoparticle concentration and it is the essential variable for the optimization process. | Aleksander Lekomtsev Amin Keykhosravi Mehdi Bahari Moghaddam Reza Daneshfar Omid Rezvanjou | 2022 | Petroleum2022，8，3： | 2 |
| 3 | Determination of 13--sitosterol and cholesterol in oils after reverse micelles with Triton X-100 coupled with ultrasound-assisted back- extraction by a water/chloroform binary system prior to gas chromatography with flame ionization detection 显示文摘 | Fatemeh Kardani Ali Daneshfar Reza Sahrai | 2011 | Analytiea Chimiea Acta2011，701，： | 1 |
| 4 | On the prediction of methane adsorption in shale using grey wolf optimizer support vector machine approach显示文摘With the advancement of technology,gas shales have become one of the most prominent energy sources all over the world.Therefore,estimating the amount of adsorbed gas in shale resources is necessary for the technical and economic foresight of the production operations.This paper presents a novel machine learning method called grey wolf optimizer support vector machine(GWO-SVM)to predict adsorbed gas.For this purpose,a data set containing temperature,pressure,total organic carbon(TOC),and humidity has been collected from several sources,and the GWO-SVM model was created based on it.The results show that this model has R-squared and root mean square error equal to 0.982 and 0.08,respectively.Also,the results ensure that the proposed model gives an excellent prediction of the amount of adsorbed gas compared to previously proposed models.Besides,according to the sensitivity analysis,among the input parameters,humidity has the highest effect on gas adsorption. | Rahmad Syah Mohammad Hossein Towfighi Naeem Reza Daneshfar Hossein Dehdar Bahram Soltani Soulgani | 2022 | Petroleum2022，8，2： | 1 |
| 5 | Determination of 13-sitosterol and holesterol in oils after reverse micelles with triton X-100 coupled with ultrasound -assisted back- extraction by a water/chloroform binary system prior to gas chromatography with flame ionization detection 显示文摘 | Fatemeh Kardani Ali Daneshfar Reza Sahrai | 2011 | Analytica Chimica Acta2011，701，： | 1 |
| 6 | On the prediction of geochemical parameters(TOC,S1 and S2)by considering well log parameters using ANFIS and LSSVM strategies显示文摘Geochemical parameters are useful properties to enhance hydrocarbon exploration certainty.Though,attaining these parameters,for instance total organic carbon(TOC),volatile and residual hydrocarbon(S1&S2)is a challenge for geologists due to the high cost and time consumption.Therefore,addressing this issue has become an interesting subject for many researchers.As a result,on the ground of conventional well logs,vast kinds of methods,for example,back propagation artificial neural network(BPANN),have been introduced to solve this problem.Implementing these kinds of methods brings scientists tremendous amounts of information related to the richness of organic matter in a meantime.However,the precision of the aforementioned method is inadequate and BPANN is affected negatively by local optimum.Therefore,current study cope with this issue and alleviate the uncertainty,Least Squares Support Vector Machine(LSSVM)and Adaptive-Neuro Fuzzy Inference System(ANFIS)algorithms cooperating with the particle swarm optimization(PSO)were suggested as a suitable method to increase the precision of estimating geochemical factors.The data bank for this research was attained from available sources of Shahejie formation from Bohai bay basin located in China,which consists of geochemical and well logging information.Outputs of this study illustrated that ANFIS-PSO and LSSVMPSO have a great ability to estimate geochemical parameters.The values of R^(2) obtained for these two models in order to predict the output parameters of TOC,S_(1) and S_(2) are equal to 0.6846&0.785,0.6864&0.778,and 0.7343&0.8128,respectively.The statistical comparison between these models shows that LSSVM-PSO shows a better performance compared to another model.Also,a new attempt was implemented to evaluate the impacts of input parameters on the outputs and the results of sensitivity analysis suggest that transit interval time had the greatest effect on the output parameters. | Danial Ahangari Reza Daneshfar Mohammad Zakeri Siavash Ashoori Bahram Soltani Soulgani | 2022 | Petroleum2022，8，2： | 0 |
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