Performance Characterization Of CQ-? Type Nano Intelligent Oil Displacement Agent And Indoor Oil Displacement Experiment Research

At present,with the increasing demand for oil and gas resources,the development of low-permeability reservoirs has received more and more attention from the industry.However,most low-permeability oil reservoirs face the technical difficulties of"no injection or production".This type of problem is particularly prominent in a low-permeability oil field in the Ordos Basin,which has seriously affected the overall production increase.In order to solve this problem and improve the oil recovery of low-permeability oil reservoirs,this thesis analyzes and synthesizes a new type of CQ-?nano intelligent oil-displacing agent suitable for this block according to the reservoir characteristics of the oil reservoir block.Used a series of characterization methods to study its performance,and explored the best process parameters of the oil-displacing agent through indoor oil-displacing experiments.The following research results have been achieved:(1)The sol-gel method was used to prepare nano-SiO₂ particles,and the SPSS software was used to analyze the significance of the influence of each factor.The order of the significance of the factors affecting the particle size of the nano-SiO₂ was:reaction time>ammonia(NH₃.H₂O)Dosage>Ethyl Orthosilicate(TOES)Dosage>Distilled Water(H₂O)Dosage>Ethanol(C₂H₅OH)Dosage>Reaction Temperature.Using Design Expert software,through the Box-Behnken analysis of the response surface module,four factors with significant influence were obtained.The optimal conditions are:the amount of TEOS is 8.11m L,the amount of NH3.H₂O is 7.65m L,the reaction time is 13.30 h,and the amount of H₂O is 3.16m L.Using silane coupling agent as modifier and adding APES surfactant to modify the surface of the previously synthesized nano-SiO₂,the CQ-?type nano intelligent oil-displacing agent was synthesized.(2)Using Laboratory instrument to characterize and evaluate the performance of CQ-?agent.The results show that the bending vibration absorption peak intensity of Si-OH on the surface of the CQ-?agent is significantly reduced,and organic groups are grafted to the surface;its appearance is milky white particles,and the microscopic appearance is an amorphous uniform approximate spherical body;The particle size distribution is about 33nm;as the concentration of CQ-?agent increases,its contact angle gradually increases,from hydrophilic to amphiphilic and finally to lipophilic,when the concentration is 0.3wt%,Its contact angle is114.86°,and the wettability for oil displacement is the best at this time;with the increase of the mass concentration of CQ-?agent,the oil-water interfacial tension gradually decreases.When the concentration is 0.3wt%,the oil-water interfacial tension Reach the lowest 2.765m N/m;with the increase of the mass concentration of CQ-?agent,its adsorption capacity gradually increases,its mass concentration increases to 0.5wt%,and the adsorption capacity reaches 15.60mg/g;as the CQ-?agent disperses With the increase of liquid concentration,the start-up pressure gradient gradually decreases,and then tends to be flat,stabilizing at0.035MPa/m.(3)The LDY-? core flow experiment device was used to conduct indoor oil displacement experiments to study the influence of salinity,mass concentration temperature and injection volume on oil displacement effects.The experimental results show that the degree of formation water salinity in the studied conditions has a greater impact on the oil displacement effect,and the CQ-?agent has a better oil displacement effect when acting on low permeability reservoirs,which is mainly due to its small Molecular effect,wedge squeezing effect,transforming wettability and reducing oil-water interfacial tension can further improve oil recovery on the basis of water-flooding oil balance,and the improvement range is between1.07%and 10.23%.The optimal injection process parameters are:30000mg/L low salinity,0.3wt%concentration,60?and 0.3PV injection volume.(4)Use the fuzzy neural network to establish the CQ-?agent recovery factor improvement grade prediction model.After training,verification and testing,the predicted output value has a high correlation with the expected output value,indicating that the model can be used for oil displacement agent recovery.Accurate prediction of yield improvement grade.