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| 1 | The effect of nanoparticles on reservoir wettability alteration:a critical review显示文摘A novel concept of treating oil reservoirs by nanofluids is being developed to improve oil recovery and reduce the trapped oil in hydrocarbon reservoirs.Nanoparticles show great potential in enhancing oil recovery under ambient conditions.In this paper,the approaches of wettability alteration by using nanofluid,stability of nanofluids,and the most reliable wettability alteration mechanisms associated with variant types of nanoparticles have been reviewed.Moreover,the parameters that have a significant influence on nanofluid flooding have been discussed.Finally,the recent studies of the effect of nanoparticles on wettability alteration have been summarised and analysed.Furthermore,this paper presents possible opportunities and challenges regarding wettability alteration using nanofluids. | Hilmy Eltoum Yu-Long Yang Ji-Rui Hou | 2021 | Petroleum Science2021，18，1： | 3 |
| 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 |
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