Less than 10% of oil is usually recovered from liquid-rich shales and this leaves much room for improvement, while water injection into shale formation is virtually impossible because of the extremely low permeability...Less than 10% of oil is usually recovered from liquid-rich shales and this leaves much room for improvement, while water injection into shale formation is virtually impossible because of the extremely low permeability of the formation matrix. Injecting carbon dioxide（CO2） into oil shale formations can potentially improve oil recovery. Furthermore, the large surface area in organicrich shale could permanently store CO2 without jeopardizing the formation integrity. This work is a mechanism study of evaluating the effectiveness of CO2-enhanced oil shale recovery and shale formation CO2 sequestration capacity using numerical simulation. Petrophysical and fluid properties similar to the Bakken Formation are used to set up the base model for simulation. Result shows that the CO_2 injection could increase the oil recovery factor from7.4% to 53%. In addition, petrophysical characteristics such as in situ stress changes and presence of a natural fracture network in the shale formation are proven to have impacts on subsurface CO2 flow. A response surface modeling approach was applied to investigate the interaction between parameters and generate a proxy model for optimizing oil recovery and CO2 injectivity.展开更多
Carbon dioxide(CO2) flooding is one of the most globally used EOR processes to enhance oil recovery.However,the low gas viscosity and density result in gas channeling and gravity override which lead to poor sweep effi...Carbon dioxide(CO2) flooding is one of the most globally used EOR processes to enhance oil recovery.However,the low gas viscosity and density result in gas channeling and gravity override which lead to poor sweep efficiency.Foam application for mobility control is a promising technology to increase the gas viscosity,lower the mobility and improve the sweep efficiency in the reservoir.Foam is generated in the reservoir by co-injection of surfactant solutions and gas.Although there are many surfactants that can be used for such purpose,their performance with supercritical CO2(ScCO2) is weak causing poor or loss of mobility control.This experimental study evaluates a newly developed surfactant(CNF) that was introduced for ScCO2 mobility control in comparison with a common foaming agent,anionic alpha olefin sulfonate(AOS) surfactant.Experimental work was divided into three stages:foam static tests,interfacial tension measurements,and foam dynamic tests.Both surfactants were investigated at different conditions.In general,results show that both surfactants are good foaming agents to reduce the mobility of ScCO2 with better performance of CNF surfactant.Shaking tests in the presence of crude oil show that the foam life for CNF extends to more than 24 h but less than that for AOS.Moreover,CNF features lower critical micelle concentration(CMC),higher adsorption,and smaller area/molecule at the liquid-air interface.Furthermore,entering,spreading,and bridging coefficients indicate that CNF surfactant produces very stable foam with light crude oil in both deionized and saline water,whereas AOS was stable only in deionized water.At all conditions for mobility reduction evaluation,CNF exhibits stronger flow resistance,higher foam viscosity,and higher mobility reduction factor than that of AOS surfactant.In addition,CNF and ScCO2 simultaneous injection produced 8.83% higher oil recovery than that of the baseline experiment and 7.87% higher than that of AOS.Pressure drop profiles for foam flooding using CNF was slightly higher than that of AOS indicating that CNF is better in terms of foam-oil tolerance which resulted in higher oil recovery.展开更多
Using artificial intelligence(AI) and machine learning(ML) techniques, we developed and validated the smart proxy models for history matching of reservoir simulation, sensitivity analysis, and uncertainty assessment b...Using artificial intelligence(AI) and machine learning(ML) techniques, we developed and validated the smart proxy models for history matching of reservoir simulation, sensitivity analysis, and uncertainty assessment by artificial neural network(ANN). The smart proxy models were applied on two cases, the first case study investigated the application of a proxy model for calibrating a reservoir simulation model based on historical data and predicting well production while the second case study investigated the application of an ANN-based proxy model for fast-track modeling of CO2 enhanced oil recovery, aiming at the prediction of the reservoir pressure and phase saturation distribution at injection stage and post-injection stage. The prediction effects for both cases are promising. While a single run of basic numerical simulation model takes hours to days, the smart proxy model runs in a matter of seconds, saving 98.9% of calculating time. The results of these case studies demonstrate the advantage of the proposed workflow for addressing the high run-time, computational time and computational cost of numerical simulation models. In addition, these proxy models predict the outputs of reservoir simulation models with high accuracy.展开更多
基金support from the Warwick Energy Group and University of Oklahoma to publish this work
文摘Less than 10% of oil is usually recovered from liquid-rich shales and this leaves much room for improvement, while water injection into shale formation is virtually impossible because of the extremely low permeability of the formation matrix. Injecting carbon dioxide（CO2） into oil shale formations can potentially improve oil recovery. Furthermore, the large surface area in organicrich shale could permanently store CO2 without jeopardizing the formation integrity. This work is a mechanism study of evaluating the effectiveness of CO2-enhanced oil shale recovery and shale formation CO2 sequestration capacity using numerical simulation. Petrophysical and fluid properties similar to the Bakken Formation are used to set up the base model for simulation. Result shows that the CO_2 injection could increase the oil recovery factor from7.4% to 53%. In addition, petrophysical characteristics such as in situ stress changes and presence of a natural fracture network in the shale formation are proven to have impacts on subsurface CO2 flow. A response surface modeling approach was applied to investigate the interaction between parameters and generate a proxy model for optimizing oil recovery and CO2 injectivity.
文摘Carbon dioxide(CO2) flooding is one of the most globally used EOR processes to enhance oil recovery.However,the low gas viscosity and density result in gas channeling and gravity override which lead to poor sweep efficiency.Foam application for mobility control is a promising technology to increase the gas viscosity,lower the mobility and improve the sweep efficiency in the reservoir.Foam is generated in the reservoir by co-injection of surfactant solutions and gas.Although there are many surfactants that can be used for such purpose,their performance with supercritical CO2(ScCO2) is weak causing poor or loss of mobility control.This experimental study evaluates a newly developed surfactant(CNF) that was introduced for ScCO2 mobility control in comparison with a common foaming agent,anionic alpha olefin sulfonate(AOS) surfactant.Experimental work was divided into three stages:foam static tests,interfacial tension measurements,and foam dynamic tests.Both surfactants were investigated at different conditions.In general,results show that both surfactants are good foaming agents to reduce the mobility of ScCO2 with better performance of CNF surfactant.Shaking tests in the presence of crude oil show that the foam life for CNF extends to more than 24 h but less than that for AOS.Moreover,CNF features lower critical micelle concentration(CMC),higher adsorption,and smaller area/molecule at the liquid-air interface.Furthermore,entering,spreading,and bridging coefficients indicate that CNF surfactant produces very stable foam with light crude oil in both deionized and saline water,whereas AOS was stable only in deionized water.At all conditions for mobility reduction evaluation,CNF exhibits stronger flow resistance,higher foam viscosity,and higher mobility reduction factor than that of AOS surfactant.In addition,CNF and ScCO2 simultaneous injection produced 8.83% higher oil recovery than that of the baseline experiment and 7.87% higher than that of AOS.Pressure drop profiles for foam flooding using CNF was slightly higher than that of AOS indicating that CNF is better in terms of foam-oil tolerance which resulted in higher oil recovery.
文摘Using artificial intelligence(AI) and machine learning(ML) techniques, we developed and validated the smart proxy models for history matching of reservoir simulation, sensitivity analysis, and uncertainty assessment by artificial neural network(ANN). The smart proxy models were applied on two cases, the first case study investigated the application of a proxy model for calibrating a reservoir simulation model based on historical data and predicting well production while the second case study investigated the application of an ANN-based proxy model for fast-track modeling of CO2 enhanced oil recovery, aiming at the prediction of the reservoir pressure and phase saturation distribution at injection stage and post-injection stage. The prediction effects for both cases are promising. While a single run of basic numerical simulation model takes hours to days, the smart proxy model runs in a matter of seconds, saving 98.9% of calculating time. The results of these case studies demonstrate the advantage of the proposed workflow for addressing the high run-time, computational time and computational cost of numerical simulation models. In addition, these proxy models predict the outputs of reservoir simulation models with high accuracy.
