Subsurface geothermal energy storage has greater potential than other energy storage strategies in terms of capacity scale and time duration.Carbon dioxide(CO_(2))is regarded as a potential medium for energy storage d...Subsurface geothermal energy storage has greater potential than other energy storage strategies in terms of capacity scale and time duration.Carbon dioxide(CO_(2))is regarded as a potential medium for energy storage due to its superior thermal properties.Moreover,the use of CO_(2)plumes for geothermal energy storage mitigates the greenhouse effect by storing CO_(2)in geological bodies.In this work,an integrated framework is proposed for synergistic geothermal energy storage and CO_(2)sequestration and utilization.Within this framework,CO_(2)is first injected into geothermal layers for energy accumulation.The resultant high-energy CO_(2)is then introduced into a target oil reservoir for CO_(2)utilization and geothermal energy storage.As a result,CO_(2)is sequestrated in the geological oil reservoir body.The results show that,as high-energy CO_(2)is injected,the average temperature of the whole target reservoir is greatly increased.With the assistance of geothermal energy,the geological utilization efficiency of CO_(2)is higher,resulting in a 10.1%increase in oil displacement efficiency.According to a storage-potential assessment of the simulated CO_(2)site,110 years after the CO_(2)injection,the utilization efficiency of the geological body will be as high as 91.2%,and the final injection quantity of the CO_(2)in the site will be as high as 9.529×10^(8)t.After 1000 years sequestration,the supercritical phase dominates in CO_(2)sequestration,followed by the liquid phase and then the mineralized phase.In addition,CO_(2)sequestration accounting for dissolution trapping increases significantly due to the presence of residual oil.More importantly,CO_(2)exhibits excellent performance in storing geothermal energy on a large scale;for example,the total energy stored in the studied geological body can provide the yearly energy supply for over 3.5×10^(7) normal households.Application of this integrated approach holds great significance for large-scale geothermal energy storage and the achievement of carbon neutrality.展开更多
Surfactant flooding is a well-known chemical approach for enhancing oil recovery.Surfactant flooding has the disadvantage that it cannot withstand the harsh reservoir conditions.Improvements in oil recovery and releas...Surfactant flooding is a well-known chemical approach for enhancing oil recovery.Surfactant flooding has the disadvantage that it cannot withstand the harsh reservoir conditions.Improvements in oil recovery and release are made possible by the use of nanoparticles and surfactants and CO_(2)co-injection because they generate stable foam,reduce the interfacial tension(IFT)between water and oil,cause emulsions to spontaneously form,change the wettability of porous media,and change the characteristics of flow.In the current work,the simultaneous injection of SiO_(2),Al2O3 nanoparticles,anionic surfactant SDS,and CO_(2)in various scenarios were evaluated to determine the microscopic and macroscopic efficacy of heavy oil recovery.IFT(interfacial tension)was reduced by 44%when the nanoparticles and SDS(2000 ppm)were added,compared to a reduction of roughly 57%with SDS only.SDS-stabilized CO_(2)foam flooding,however,is unstable due to the adsorption of SDS in the rock surfaces as well as in heavy oil.To assess foam's potential to shift CO_(2)from the high permeability zone(the thief zone)into the low permeability zone,directly visualizing micromodel flooding was successfully executed(upswept oil-rich zone).Based on typical reservoir permeability fluctuations,the permeability contrast(defined as the ratio of high permeability to low permeability)for the micromodel flooding was selected.However,the results of the experiment demonstrated that by utilizing SDS and nanoparticles,minimal IFT was reached.The addition of nanoparticles to surfactant solutions,however,greatly boosted oil recovery,according to the findings of flooding studies.The ultimate oil recovery was generally improved more by the anionic surfactant(SDS)solution including nanoparticles than by the anionic surfactant(SDS)alone.展开更多
This paper reviews the utilization of Big Data analytics,as an emerging trend,in the upstream and downstream oil and gas industry.Big Data or Big Data analytics refers to a new technology which can be employed to hand...This paper reviews the utilization of Big Data analytics,as an emerging trend,in the upstream and downstream oil and gas industry.Big Data or Big Data analytics refers to a new technology which can be employed to handle large datasets which include six main characteristics of volume,variety,velocity,veracity,value,and complexity.With the recent advent of data recording sensors in exploration,drilling,and production operations,oil and gas industry has become a massive data intensive industry.Analyzing seismic and micro-seismic data,improving reservoir characterization and simulation,reducing drilling time and increasing drilling safety,optimization of the performance of production pumps,improved petrochemical asset management,improved shipping and transportation,and improved occupational safety are among some of the applications of Big Data in oil and gas industry.Although the oil and gas industry has become more interested in utilizing Big Data analytics recently,but,there are still challenges mainly due to lack of business support and awareness about the Big Data within the industry.Furthermore,quality of the data and understanding the complexity of the problem are also among the challenging parameters facing the application of Big Data.展开更多
Surfactant foam stability gets a lot of interest while posing a significant obstacle to many industrial operations.One of the viable solutions for addressing gas mobility concerns and boosting reservoir fluid sweep ef...Surfactant foam stability gets a lot of interest while posing a significant obstacle to many industrial operations.One of the viable solutions for addressing gas mobility concerns and boosting reservoir fluid sweep efficiency during solvent-based enhanced heavy oil recovery processes is foam formation.The synergistic effect of nanoparticles and surfactants in a porous reservoir media can help create a more durable and sturdier foam.This study aims to see how well a combination of the nanoparticles(NPs)and surfactant can generate foam for controlling gas mobility and improving oil recovery.This research looked at the effects of silicon and aluminum oxide nanoparticles on the bulk and dynamic stability of sodium dodecyl surfactant(SDS)-foam in the presence and absence of oil.Normalized foam height,liquid drainage,half-decay life,nanoparticle deposition,and bubble size distribution of the generated foams with time were used to assess static foam stability in the bulk phase,while dynamic stability was studied in the micromodel.To understand the processes of foam stabilization by nanoparticles,the microscopic images of foam and the shape of bubbles were examined.When nanoparticles were applied in foamability testing in bulk and dynamic phase,the foam generation and stability were improved by 23%and 17%,respectively.In comparison to surfactant alone,adding nanoparticles to surfactant solutions leads to a more significant pressure drop of 17.34 psi for SiO_(2)and 14.86 psi for Al_(2)O_(3)NPs and,as a result,a higher reduction in gas mobility which ultimately assists in enhancing oil recovery.展开更多
The low cost of the injected solvent,which can be also recovered and recycled,and the applicability of VAPEX technique in thin reservoirs are among the main advantages of VAPEX process compared to thermal heavy oil re...The low cost of the injected solvent,which can be also recovered and recycled,and the applicability of VAPEX technique in thin reservoirs are among the main advantages of VAPEX process compared to thermal heavy oil recovery techniques.In this research,an extensive experimental investigation is carried out to first evaluate the technical feasibility of utilization of various solvents for VAPEX process.Then the effect of drainage height on the stabilized drainage rate in VAPEX process was studied by conducting series of experiments in two large-scale 2D VAPEX models of 24.5 cm and 47.5 cm heights.Both models were packed with low permeability Ottawa sand(#530)and saturated with a heavy oil sample from Saskatchewan heavy oil reservoirs with viscosity of 5650 mPa s.Propane,butane,methane,carbon dioxide,propane/carbon dioxide(70%/30%)and propane/methane(70%/30%)were considered as respective solvents for the experiments,and a total of twelve VAPEX tests were carried out.Moreover,separate experiments were carried out at the end of each VAPEX experiment to measure the asphaltene precipitation at various locations of the VAPEX models.It was found that injecting propane would result in the highest drainage rate and oil recovery factor.Further analysis of results showed stabilized drainage rate significantly increased in the larger physical model.展开更多
Pore size determination of hydrocarbon reservoirs is one of the main challenging areas in reservoir studies.Precise estimation of this parameter leads to enhance the reservoir simulation,process evaluation,and further...Pore size determination of hydrocarbon reservoirs is one of the main challenging areas in reservoir studies.Precise estimation of this parameter leads to enhance the reservoir simulation,process evaluation,and further forecasting of reservoir behavior.Hence,it is of great importance to estimate the pore size of reservoir rocks with an appropriate accuracy.In the present study,a modified J-function was developed and applied to determine the pore radius in one of the hydrocarbon reservoir rocks located in the Middle East.The capillary pressure data vs.water saturation(PceSw)as well as routine reservoir core analysis include porosity(4)and permeability(k)were used to develop the J-function.First,the normalized porosity(4z),the rock quality index(RQI),and the flow zone indicator(FZI)concepts were used to categorize all data into discrete hydraulic flow units(HFU)containing unique pore geometry and bedding characteristics.Thereafter,the modified J-function was used to normalize all capillary pressure curves corresponding to each of predetermined HFU.The results showed that the reservoir rock was classified into five separate rock types with the definite HFU and reservoir pore geometry.Eventually,the pore radius for each of these HFUs was determined using a developed equation obtained by normalized J-function corresponding to each HFU.The proposed equation is a function of reservoir rock characteristics including 4z,FZI,lithology index(J*),and pore size distribution index(3).This methodology used,the reservoir under study was classified into five discrete HFU with unique equations for permeability,normalized J-function and pore size.The proposed technique is able to apply on any reservoir to determine the pore size of the reservoir rock,specially the one with high range of heterogeneity in the reservoir rock properties.展开更多
As the price of oil decreases,it is becoming increasingly important for oil companies to operate in the most costeffective manner.This problem is especially apparent in Western Canada,where most oil production is depe...As the price of oil decreases,it is becoming increasingly important for oil companies to operate in the most costeffective manner.This problem is especially apparent in Western Canada,where most oil production is dependent on costly enhanced oil recovery(EOR)techniques such as steam-assisted gravity drainage(SAGD).Therefore,the goal of this study is to create an artificial neural network(ANN)that is capable of accurately predicting the ultimate recovery factor of oil reservoirs by steam-assisted gravity drainage(SAGD).The developed ANN model featured over 250 unique entries for oil viscosity,steam injection rate,horizontal permeability,permeability ratio,porosity,reservoir thickness,and steam injection pressure collected from literature.The collected data set was entered through a feed-forward back-propagation neural network to train,validate,and test the model to predict the recovery factor of SAGD method as accurate as possible.Results from this study revealed that the neural network was able to accurately predict recovery factors of selected projects with less than 10%error.When the neural network was exposed to a new simulation data set of 64 points,the predictions were found to have an accuracy of 82%as measured by linear regression.Finally,the feasibility of ANN to predict the recovery performance of one of the most complicated enhanced heavy oil recovery techniques with reasonable accuracy was confirmed.展开更多
In this study,a sandpack model with porosity and permeability of 32.3%and 9.4 D,and a heavy crude oil with viscosity of 6430 mPa.s were used to represent a typical thin heavy oil formation.First,different ratios of C3...In this study,a sandpack model with porosity and permeability of 32.3%and 9.4 D,and a heavy crude oil with viscosity of 6430 mPa.s were used to represent a typical thin heavy oil formation.First,different ratios of C3H8 to CH4 stream were prepared and their performance on Cyclic Solvent Injection(CSI)method was examined to quantify the optimum solvent concentration.Second,CO2 was introduced to the optimum quantified CH4-C3H8 mixture to investigate the extent to which CSI behavior changes by partially replacement of CH4 with CO2.Results showed that ultimate oil recovery factor(RF)increased from 24.3%to 33.4%original oil in place(OOIP)when C3H8 concentration increased from 15 to 50 mol%in the CH4 stream.CSI tests with higher C3H8 concentration reached the maximum cyclic recovery with lower number of injection cycles-due to higher solubility of C3H8 compared with CH4.Solvent utilization factor(SUF)data also confirmed this as lesser volume of solvent with higher C3H8 concentration was required to produce oil.Visual observations showed that the produced foamy oil lasted longer with higher concentration of C3H8 in the solvent(5 min for 15%C3H8 e 85%CH4 case versus 180min for 50%C3H8 e 50%CH4 case).Upon addition of CO2 to the mixture,the solvent apparent solubility increased and foamy oil flow promoted.The highest cyclic C3H8-CH4 apparent solubility of 0.175 gr.solvent/100 gr.remaining oil jumped to 0.53 gr.solvent/100 gr.remaining oil when 35%mole fraction of CO2 replaced CH4.The highest ultimate oil RF of 44.11%OOIP was measured from eight cycle injection of 50%C3H8 e 15%CH4 e 35%CO2.This solvent also benefited from the longest stability of produced-oil foamy shape with recorded time of 217 min(including production time).According to the results of this experimental study,it seems that there is an optimum fraction of C3H8 in CH4 stream injection in heavy oil systems(with viscosity in the vicinity of 6430 mPa s);the concentration beyond which ultimate oil recovery factor does not increase significantly(near 50 mol%).It is speculated that last cycles do not appreciably respond to heavy oil production mainly due to asphaltene getting precipitated within the model.展开更多
基金supported by the National Key Research and Development Program of China under grant(2022YFE0206700)the financial support by the National Natural Science Foundation of China(52004320)the Science Foundation of China University of Petroleum,Beijing(2462021QNXZ012 and 2462021YJRC012)。
文摘Subsurface geothermal energy storage has greater potential than other energy storage strategies in terms of capacity scale and time duration.Carbon dioxide(CO_(2))is regarded as a potential medium for energy storage due to its superior thermal properties.Moreover,the use of CO_(2)plumes for geothermal energy storage mitigates the greenhouse effect by storing CO_(2)in geological bodies.In this work,an integrated framework is proposed for synergistic geothermal energy storage and CO_(2)sequestration and utilization.Within this framework,CO_(2)is first injected into geothermal layers for energy accumulation.The resultant high-energy CO_(2)is then introduced into a target oil reservoir for CO_(2)utilization and geothermal energy storage.As a result,CO_(2)is sequestrated in the geological oil reservoir body.The results show that,as high-energy CO_(2)is injected,the average temperature of the whole target reservoir is greatly increased.With the assistance of geothermal energy,the geological utilization efficiency of CO_(2)is higher,resulting in a 10.1%increase in oil displacement efficiency.According to a storage-potential assessment of the simulated CO_(2)site,110 years after the CO_(2)injection,the utilization efficiency of the geological body will be as high as 91.2%,and the final injection quantity of the CO_(2)in the site will be as high as 9.529×10^(8)t.After 1000 years sequestration,the supercritical phase dominates in CO_(2)sequestration,followed by the liquid phase and then the mineralized phase.In addition,CO_(2)sequestration accounting for dissolution trapping increases significantly due to the presence of residual oil.More importantly,CO_(2)exhibits excellent performance in storing geothermal energy on a large scale;for example,the total energy stored in the studied geological body can provide the yearly energy supply for over 3.5×10^(7) normal households.Application of this integrated approach holds great significance for large-scale geothermal energy storage and the achievement of carbon neutrality.
基金The Petroleum Technology Research Centre (PTRC),MITACSgraduate studies at the University of Regina all provided funding for this study
文摘Surfactant flooding is a well-known chemical approach for enhancing oil recovery.Surfactant flooding has the disadvantage that it cannot withstand the harsh reservoir conditions.Improvements in oil recovery and release are made possible by the use of nanoparticles and surfactants and CO_(2)co-injection because they generate stable foam,reduce the interfacial tension(IFT)between water and oil,cause emulsions to spontaneously form,change the wettability of porous media,and change the characteristics of flow.In the current work,the simultaneous injection of SiO_(2),Al2O3 nanoparticles,anionic surfactant SDS,and CO_(2)in various scenarios were evaluated to determine the microscopic and macroscopic efficacy of heavy oil recovery.IFT(interfacial tension)was reduced by 44%when the nanoparticles and SDS(2000 ppm)were added,compared to a reduction of roughly 57%with SDS only.SDS-stabilized CO_(2)foam flooding,however,is unstable due to the adsorption of SDS in the rock surfaces as well as in heavy oil.To assess foam's potential to shift CO_(2)from the high permeability zone(the thief zone)into the low permeability zone,directly visualizing micromodel flooding was successfully executed(upswept oil-rich zone).Based on typical reservoir permeability fluctuations,the permeability contrast(defined as the ratio of high permeability to low permeability)for the micromodel flooding was selected.However,the results of the experiment demonstrated that by utilizing SDS and nanoparticles,minimal IFT was reached.The addition of nanoparticles to surfactant solutions,however,greatly boosted oil recovery,according to the findings of flooding studies.The ultimate oil recovery was generally improved more by the anionic surfactant(SDS)solution including nanoparticles than by the anionic surfactant(SDS)alone.
文摘This paper reviews the utilization of Big Data analytics,as an emerging trend,in the upstream and downstream oil and gas industry.Big Data or Big Data analytics refers to a new technology which can be employed to handle large datasets which include six main characteristics of volume,variety,velocity,veracity,value,and complexity.With the recent advent of data recording sensors in exploration,drilling,and production operations,oil and gas industry has become a massive data intensive industry.Analyzing seismic and micro-seismic data,improving reservoir characterization and simulation,reducing drilling time and increasing drilling safety,optimization of the performance of production pumps,improved petrochemical asset management,improved shipping and transportation,and improved occupational safety are among some of the applications of Big Data in oil and gas industry.Although the oil and gas industry has become more interested in utilizing Big Data analytics recently,but,there are still challenges mainly due to lack of business support and awareness about the Big Data within the industry.Furthermore,quality of the data and understanding the complexity of the problem are also among the challenging parameters facing the application of Big Data.
基金This research is funded by graduate studies of the University of Regina,Petroleum Technology Research Centre(PTRC),and MITACS.
文摘Surfactant foam stability gets a lot of interest while posing a significant obstacle to many industrial operations.One of the viable solutions for addressing gas mobility concerns and boosting reservoir fluid sweep efficiency during solvent-based enhanced heavy oil recovery processes is foam formation.The synergistic effect of nanoparticles and surfactants in a porous reservoir media can help create a more durable and sturdier foam.This study aims to see how well a combination of the nanoparticles(NPs)and surfactant can generate foam for controlling gas mobility and improving oil recovery.This research looked at the effects of silicon and aluminum oxide nanoparticles on the bulk and dynamic stability of sodium dodecyl surfactant(SDS)-foam in the presence and absence of oil.Normalized foam height,liquid drainage,half-decay life,nanoparticle deposition,and bubble size distribution of the generated foams with time were used to assess static foam stability in the bulk phase,while dynamic stability was studied in the micromodel.To understand the processes of foam stabilization by nanoparticles,the microscopic images of foam and the shape of bubbles were examined.When nanoparticles were applied in foamability testing in bulk and dynamic phase,the foam generation and stability were improved by 23%and 17%,respectively.In comparison to surfactant alone,adding nanoparticles to surfactant solutions leads to a more significant pressure drop of 17.34 psi for SiO_(2)and 14.86 psi for Al_(2)O_(3)NPs and,as a result,a higher reduction in gas mobility which ultimately assists in enhancing oil recovery.
文摘The low cost of the injected solvent,which can be also recovered and recycled,and the applicability of VAPEX technique in thin reservoirs are among the main advantages of VAPEX process compared to thermal heavy oil recovery techniques.In this research,an extensive experimental investigation is carried out to first evaluate the technical feasibility of utilization of various solvents for VAPEX process.Then the effect of drainage height on the stabilized drainage rate in VAPEX process was studied by conducting series of experiments in two large-scale 2D VAPEX models of 24.5 cm and 47.5 cm heights.Both models were packed with low permeability Ottawa sand(#530)and saturated with a heavy oil sample from Saskatchewan heavy oil reservoirs with viscosity of 5650 mPa s.Propane,butane,methane,carbon dioxide,propane/carbon dioxide(70%/30%)and propane/methane(70%/30%)were considered as respective solvents for the experiments,and a total of twelve VAPEX tests were carried out.Moreover,separate experiments were carried out at the end of each VAPEX experiment to measure the asphaltene precipitation at various locations of the VAPEX models.It was found that injecting propane would result in the highest drainage rate and oil recovery factor.Further analysis of results showed stabilized drainage rate significantly increased in the larger physical model.
文摘Pore size determination of hydrocarbon reservoirs is one of the main challenging areas in reservoir studies.Precise estimation of this parameter leads to enhance the reservoir simulation,process evaluation,and further forecasting of reservoir behavior.Hence,it is of great importance to estimate the pore size of reservoir rocks with an appropriate accuracy.In the present study,a modified J-function was developed and applied to determine the pore radius in one of the hydrocarbon reservoir rocks located in the Middle East.The capillary pressure data vs.water saturation(PceSw)as well as routine reservoir core analysis include porosity(4)and permeability(k)were used to develop the J-function.First,the normalized porosity(4z),the rock quality index(RQI),and the flow zone indicator(FZI)concepts were used to categorize all data into discrete hydraulic flow units(HFU)containing unique pore geometry and bedding characteristics.Thereafter,the modified J-function was used to normalize all capillary pressure curves corresponding to each of predetermined HFU.The results showed that the reservoir rock was classified into five separate rock types with the definite HFU and reservoir pore geometry.Eventually,the pore radius for each of these HFUs was determined using a developed equation obtained by normalized J-function corresponding to each HFU.The proposed equation is a function of reservoir rock characteristics including 4z,FZI,lithology index(J*),and pore size distribution index(3).This methodology used,the reservoir under study was classified into five discrete HFU with unique equations for permeability,normalized J-function and pore size.The proposed technique is able to apply on any reservoir to determine the pore size of the reservoir rock,specially the one with high range of heterogeneity in the reservoir rock properties.
文摘As the price of oil decreases,it is becoming increasingly important for oil companies to operate in the most costeffective manner.This problem is especially apparent in Western Canada,where most oil production is dependent on costly enhanced oil recovery(EOR)techniques such as steam-assisted gravity drainage(SAGD).Therefore,the goal of this study is to create an artificial neural network(ANN)that is capable of accurately predicting the ultimate recovery factor of oil reservoirs by steam-assisted gravity drainage(SAGD).The developed ANN model featured over 250 unique entries for oil viscosity,steam injection rate,horizontal permeability,permeability ratio,porosity,reservoir thickness,and steam injection pressure collected from literature.The collected data set was entered through a feed-forward back-propagation neural network to train,validate,and test the model to predict the recovery factor of SAGD method as accurate as possible.Results from this study revealed that the neural network was able to accurately predict recovery factors of selected projects with less than 10%error.When the neural network was exposed to a new simulation data set of 64 points,the predictions were found to have an accuracy of 82%as measured by linear regression.Finally,the feasibility of ANN to predict the recovery performance of one of the most complicated enhanced heavy oil recovery techniques with reasonable accuracy was confirmed.
基金The“Faculty of Graduate Studies and Research(FGSR)of University of Regina”and also“Petroleum Technology Research Centre”are acknowledged for providing financial support in order to carry out this experimental project.
文摘In this study,a sandpack model with porosity and permeability of 32.3%and 9.4 D,and a heavy crude oil with viscosity of 6430 mPa.s were used to represent a typical thin heavy oil formation.First,different ratios of C3H8 to CH4 stream were prepared and their performance on Cyclic Solvent Injection(CSI)method was examined to quantify the optimum solvent concentration.Second,CO2 was introduced to the optimum quantified CH4-C3H8 mixture to investigate the extent to which CSI behavior changes by partially replacement of CH4 with CO2.Results showed that ultimate oil recovery factor(RF)increased from 24.3%to 33.4%original oil in place(OOIP)when C3H8 concentration increased from 15 to 50 mol%in the CH4 stream.CSI tests with higher C3H8 concentration reached the maximum cyclic recovery with lower number of injection cycles-due to higher solubility of C3H8 compared with CH4.Solvent utilization factor(SUF)data also confirmed this as lesser volume of solvent with higher C3H8 concentration was required to produce oil.Visual observations showed that the produced foamy oil lasted longer with higher concentration of C3H8 in the solvent(5 min for 15%C3H8 e 85%CH4 case versus 180min for 50%C3H8 e 50%CH4 case).Upon addition of CO2 to the mixture,the solvent apparent solubility increased and foamy oil flow promoted.The highest cyclic C3H8-CH4 apparent solubility of 0.175 gr.solvent/100 gr.remaining oil jumped to 0.53 gr.solvent/100 gr.remaining oil when 35%mole fraction of CO2 replaced CH4.The highest ultimate oil RF of 44.11%OOIP was measured from eight cycle injection of 50%C3H8 e 15%CH4 e 35%CO2.This solvent also benefited from the longest stability of produced-oil foamy shape with recorded time of 217 min(including production time).According to the results of this experimental study,it seems that there is an optimum fraction of C3H8 in CH4 stream injection in heavy oil systems(with viscosity in the vicinity of 6430 mPa s);the concentration beyond which ultimate oil recovery factor does not increase significantly(near 50 mol%).It is speculated that last cycles do not appreciably respond to heavy oil production mainly due to asphaltene getting precipitated within the model.
