Miscible CO_(2)injection appears to be an important enhanced oil recovery technique for improving sweep efficiency and eliminating CO_(2)-oil interfacial tension resulting in up to 10%higher oil recovery compared to i...Miscible CO_(2)injection appears to be an important enhanced oil recovery technique for improving sweep efficiency and eliminating CO_(2)-oil interfacial tension resulting in up to 10%higher oil recovery compared to immiscible flooding,in addition to the environmental benefits of reducing greenhouse gas emissions through carbon capturing utilising and storage(CCUS).Moreover,this technique could be similarly applicable to natural gas and nitrogen projects to increase oil recovery and to reduce the associated gas flaring.However,miscible displacement may not be achievable for all reservoirs,in particular,reservoirs with high temperature where high injection pressure would be needed to reach miscibility which likely exceeds the formation fracture pressure.Therefore,to further achieve reservoirs’potential,there is a pressing need to explore a viable means to decrease the miscibility pressure,and thus expand the application envelop of miscible gas injection in reservoirs with high temperatures.In this work,we aim to provide insights into minimum miscibility pressure(MMP)reduction by adding chemicals into CO_(2)phase during injection.We achieved this objective by performing a comprehensive review on chemical-assisted MMP reduction using different chemical additives(e.g.,alcohols,fatty acids,surfactants)and different experimental methodologies.Previous experimental studies have shown that a fraction of chemical additives can yield up to 22%of MMP reduction in CO_(2)-oil system.Based on results analysis,surfactant based chemicals were found to be more efficient compared to alcohol based chemicals in reducing the interfacial tension in the CO_(2)-oil system.Based on the current experimental results,adding chemicals to improve the miscibility and reduce the MMP in the CO_(2)-oil system appears to be a promising technique to increase oil recovery while reducing operating cost.Selection of the effective chemical additives may help to expand the application of miscible gas injection to shallow and high temperature reservoirs.Furthermore,our review provides an overall framework to screen potential chemical additives and an injection strategy to be used for miscible displacement in CO_(2)and/or gas systems.展开更多
technique.However,the main challenge in this process is the high minimum miscibility pressure(MMP)between natural gas and crude oil,which limits its application and recovery factor,especially in hightemperature reserv...technique.However,the main challenge in this process is the high minimum miscibility pressure(MMP)between natural gas and crude oil,which limits its application and recovery factor,especially in hightemperature reservoirs.Therefore,we present a novel investigation to quantify the effect of chemicalassisted MMP reduction on the oil recovery factor.Firstly,we measured the interfacial tension(IFT)of the methane-oil system in the presence of chemical or CO_(2) to calculate the MMP reduction at a constant temperature(373K)using the vanishing interfacial tension(VIT)method.Afterwards,we performed three coreflooding experiments to quantify the effect of MMP reduction on the oil recovery factor under different injection scenarios.The interfacial tension measurements show that adding a small fraction(1.5 wt%)of the tested surfactant(SOLOTERRA ME-6)achieved 9%of MMP reduction,while adding 20 wt%of CO_(2) to the methane yields 13%of MMP reduction.Then,the coreflooding results highlight the significance of achieving miscibility during gas injection,as the ultimate recovery factor increased from 65.5%under immiscible conditions to 77.2%using chemical-assisted methane,and to 79%using gas mixture after achieving near miscible condition.The results demonstrate the promising potential of the MMP reduction to signifi-cantly increase the oil recovery factor during gas injection.Furthermore,these results will likely expand the application envelop of the miscible gas injection,in addition to the environmental benefits of utilizing the produced gas by re-injection/recycling instead of flaring which contributes to reducing the greenhouse gas emissions.展开更多
The technique of Enhanced Gas Recovery by CO_(2) injection(CO_(2)-EGR)into shale reservoirs has brought increasing attention in the recent decade.CO_(2)-EGR is a complex geophysical process that is controlled by sever...The technique of Enhanced Gas Recovery by CO_(2) injection(CO_(2)-EGR)into shale reservoirs has brought increasing attention in the recent decade.CO_(2)-EGR is a complex geophysical process that is controlled by several parameters of shale properties and engineering design.Nevertheless,more challenges arise when simulating and predicting CO_(2)/CH4 displacement within the complex pore systems of shales.Therefore,the petroleum industry is in need of developing a cost-effective tool/approach to evaluate the potential of applying CO_(2) injection to shale reservoirs.In recent years,machine learning applications have gained enormous interest due to their high-speed performance in handling complex data and efficiently solving practical problems.Thus,this work proposes a solution by developing a supervised machine learning(ML)based model to preliminary evaluate CO_(2)-EGR efficiency.Data used for this work was drawn across a wide range of simulation sensitivity studies and experimental investigations.In this work,linear regression and artificial neural networks(ANNs)implementations were considered for predicting the incremental enhanced CH4.Based on the model performance in training and validation sets,our accuracy comparison showed that(ANNs)algorithms gave 15%higher accuracy in predicting the enhanced CH4 compared to the linear regression model.To ensure the model is more generalizable,the size of hidden layers of ANNs was adjusted to improve the generalization ability of ANNs model.Among ANNs models presented,ANNs of 100 hidden layer size gave the best predictive performance with the coefficient of determination(R2)of 0.78 compared to the linear regression model with R2 of 0.68.Our developed MLbased model presents a powerful,reliable and cost-effective tool which can accurately predict the incremental enhanced CH4 by CO_(2) injection in shale gas reservoirs.展开更多
基金by Petro China Tarim Oilfield Company(PetroChina).
文摘Miscible CO_(2)injection appears to be an important enhanced oil recovery technique for improving sweep efficiency and eliminating CO_(2)-oil interfacial tension resulting in up to 10%higher oil recovery compared to immiscible flooding,in addition to the environmental benefits of reducing greenhouse gas emissions through carbon capturing utilising and storage(CCUS).Moreover,this technique could be similarly applicable to natural gas and nitrogen projects to increase oil recovery and to reduce the associated gas flaring.However,miscible displacement may not be achievable for all reservoirs,in particular,reservoirs with high temperature where high injection pressure would be needed to reach miscibility which likely exceeds the formation fracture pressure.Therefore,to further achieve reservoirs’potential,there is a pressing need to explore a viable means to decrease the miscibility pressure,and thus expand the application envelop of miscible gas injection in reservoirs with high temperatures.In this work,we aim to provide insights into minimum miscibility pressure(MMP)reduction by adding chemicals into CO_(2)phase during injection.We achieved this objective by performing a comprehensive review on chemical-assisted MMP reduction using different chemical additives(e.g.,alcohols,fatty acids,surfactants)and different experimental methodologies.Previous experimental studies have shown that a fraction of chemical additives can yield up to 22%of MMP reduction in CO_(2)-oil system.Based on results analysis,surfactant based chemicals were found to be more efficient compared to alcohol based chemicals in reducing the interfacial tension in the CO_(2)-oil system.Based on the current experimental results,adding chemicals to improve the miscibility and reduce the MMP in the CO_(2)-oil system appears to be a promising technique to increase oil recovery while reducing operating cost.Selection of the effective chemical additives may help to expand the application of miscible gas injection to shallow and high temperature reservoirs.Furthermore,our review provides an overall framework to screen potential chemical additives and an injection strategy to be used for miscible displacement in CO_(2)and/or gas systems.
基金supported by a joint project with Southwest Petroleum University (China)funded by Tarim Oilfield Company (PetroChina).
文摘technique.However,the main challenge in this process is the high minimum miscibility pressure(MMP)between natural gas and crude oil,which limits its application and recovery factor,especially in hightemperature reservoirs.Therefore,we present a novel investigation to quantify the effect of chemicalassisted MMP reduction on the oil recovery factor.Firstly,we measured the interfacial tension(IFT)of the methane-oil system in the presence of chemical or CO_(2) to calculate the MMP reduction at a constant temperature(373K)using the vanishing interfacial tension(VIT)method.Afterwards,we performed three coreflooding experiments to quantify the effect of MMP reduction on the oil recovery factor under different injection scenarios.The interfacial tension measurements show that adding a small fraction(1.5 wt%)of the tested surfactant(SOLOTERRA ME-6)achieved 9%of MMP reduction,while adding 20 wt%of CO_(2) to the methane yields 13%of MMP reduction.Then,the coreflooding results highlight the significance of achieving miscibility during gas injection,as the ultimate recovery factor increased from 65.5%under immiscible conditions to 77.2%using chemical-assisted methane,and to 79%using gas mixture after achieving near miscible condition.The results demonstrate the promising potential of the MMP reduction to signifi-cantly increase the oil recovery factor during gas injection.Furthermore,these results will likely expand the application envelop of the miscible gas injection,in addition to the environmental benefits of utilizing the produced gas by re-injection/recycling instead of flaring which contributes to reducing the greenhouse gas emissions.
文摘The technique of Enhanced Gas Recovery by CO_(2) injection(CO_(2)-EGR)into shale reservoirs has brought increasing attention in the recent decade.CO_(2)-EGR is a complex geophysical process that is controlled by several parameters of shale properties and engineering design.Nevertheless,more challenges arise when simulating and predicting CO_(2)/CH4 displacement within the complex pore systems of shales.Therefore,the petroleum industry is in need of developing a cost-effective tool/approach to evaluate the potential of applying CO_(2) injection to shale reservoirs.In recent years,machine learning applications have gained enormous interest due to their high-speed performance in handling complex data and efficiently solving practical problems.Thus,this work proposes a solution by developing a supervised machine learning(ML)based model to preliminary evaluate CO_(2)-EGR efficiency.Data used for this work was drawn across a wide range of simulation sensitivity studies and experimental investigations.In this work,linear regression and artificial neural networks(ANNs)implementations were considered for predicting the incremental enhanced CH4.Based on the model performance in training and validation sets,our accuracy comparison showed that(ANNs)algorithms gave 15%higher accuracy in predicting the enhanced CH4 compared to the linear regression model.To ensure the model is more generalizable,the size of hidden layers of ANNs was adjusted to improve the generalization ability of ANNs model.Among ANNs models presented,ANNs of 100 hidden layer size gave the best predictive performance with the coefficient of determination(R2)of 0.78 compared to the linear regression model with R2 of 0.68.Our developed MLbased model presents a powerful,reliable and cost-effective tool which can accurately predict the incremental enhanced CH4 by CO_(2) injection in shale gas reservoirs.
