Introducing the novel technique for enhancing oil recovery from available petroleum reservoirs is one of the important issues in future energy demands.Among of all operative factors,wettability may be the foremost par...Introducing the novel technique for enhancing oil recovery from available petroleum reservoirs is one of the important issues in future energy demands.Among of all operative factors,wettability may be the foremost parameter affecting residual oil saturation in all stage of oil recovery.Although wettability alteration is one of the methods which enhance oil recovery from the petroleum reservoir.Recently,the studies which focused on this subject were more than the past and many contributions have been made on this area.The main objective of the current study is experimentally investigation of the two nonionic surfactants effects on altering wettability of reservoir rocks.Purpose of this work is to change the wettability to preferentially the water-wet condition.Also reducing the residual oil saturation(Sor)is the other purpose of this work.The wettability alteration of reservoir rock is measured by two main quantitative methods namely contact angle and the USBM methods.Results of this study showed that surfactant flooding is more effective in oil-wet rocks to change their wettability and consequently reducing Sor to a low value.Cedar(Zizyphus Spina Christi)is low priced,absolutely natural,and abundantly accessible in the Middle East and Central Asia.Based on the results,this material can be used as a chemical surfactant in field for enhancing oil recovery.展开更多
Water saturation determination in core laboratory is known as a cost and time consuming labor.Hitherto,many scientists attempted to estimate accurately water saturation from well-logging data which has a continuous re...Water saturation determination in core laboratory is known as a cost and time consuming labor.Hitherto,many scientists attempted to estimate accurately water saturation from well-logging data which has a continuous record without losing information.Therefore,various model were introduced to relate reservoir properties and water saturation.Since carbonate reservoir is very heterogeneous in shape and size of pore throat,the relation between water saturation and other carbonates reservoir properties is very complex,and causes considerable overall errors in water saturation calculation.By increasing the usage and improvement of soft computing methods in engineering problems,petroleum engineers have been attended them to measure the petrophysical properties of the reservoir.In this study,a radial basis function neural network(RBFNN)improved by genetic algorithm has been employed to estimate formation water saturation by using conventional well-logging data.The used logging and core data have been gathered from a carbonated formation from one of oilfield located in south-west Iran,and finally their results of the proposed model were compared with the core analysis results.By checking the testing data from another well,it showed this method had a 0.027 for mean square errors and its correlation coefficient is equal to 0.870.These results implied on high accuracy of this model for oil saturation degree estimation.While the common methods like Archie,had a 0.041 mean square error and 0.720 of the correlation coefficient,which indicate a high ability of RBF model than the other usual empirical methods.展开更多
文摘Introducing the novel technique for enhancing oil recovery from available petroleum reservoirs is one of the important issues in future energy demands.Among of all operative factors,wettability may be the foremost parameter affecting residual oil saturation in all stage of oil recovery.Although wettability alteration is one of the methods which enhance oil recovery from the petroleum reservoir.Recently,the studies which focused on this subject were more than the past and many contributions have been made on this area.The main objective of the current study is experimentally investigation of the two nonionic surfactants effects on altering wettability of reservoir rocks.Purpose of this work is to change the wettability to preferentially the water-wet condition.Also reducing the residual oil saturation(Sor)is the other purpose of this work.The wettability alteration of reservoir rock is measured by two main quantitative methods namely contact angle and the USBM methods.Results of this study showed that surfactant flooding is more effective in oil-wet rocks to change their wettability and consequently reducing Sor to a low value.Cedar(Zizyphus Spina Christi)is low priced,absolutely natural,and abundantly accessible in the Middle East and Central Asia.Based on the results,this material can be used as a chemical surfactant in field for enhancing oil recovery.
文摘Water saturation determination in core laboratory is known as a cost and time consuming labor.Hitherto,many scientists attempted to estimate accurately water saturation from well-logging data which has a continuous record without losing information.Therefore,various model were introduced to relate reservoir properties and water saturation.Since carbonate reservoir is very heterogeneous in shape and size of pore throat,the relation between water saturation and other carbonates reservoir properties is very complex,and causes considerable overall errors in water saturation calculation.By increasing the usage and improvement of soft computing methods in engineering problems,petroleum engineers have been attended them to measure the petrophysical properties of the reservoir.In this study,a radial basis function neural network(RBFNN)improved by genetic algorithm has been employed to estimate formation water saturation by using conventional well-logging data.The used logging and core data have been gathered from a carbonated formation from one of oilfield located in south-west Iran,and finally their results of the proposed model were compared with the core analysis results.By checking the testing data from another well,it showed this method had a 0.027 for mean square errors and its correlation coefficient is equal to 0.870.These results implied on high accuracy of this model for oil saturation degree estimation.While the common methods like Archie,had a 0.041 mean square error and 0.720 of the correlation coefficient,which indicate a high ability of RBF model than the other usual empirical methods.
