As an advanced polymer composites electro-kinetic geosynthetics, the electro-osmotic vertical drainage(EVD) board could drain water quickly and accelerate consolidation process. However, the drainage rate was mainly i...As an advanced polymer composites electro-kinetic geosynthetics, the electro-osmotic vertical drainage(EVD) board could drain water quickly and accelerate consolidation process. However, the drainage rate was mainly impacted by the vertical drainage capability. Therefore, vertical drainage capability at the top of EVD board was theoretically analyzed. Basic requirements for drainage at the top of the board were summed up, as well as the formula of anode pore pressure when losing the vertical drainage capability. Meanwhile, a contrast test on the top and bottom drainage capacities was conducted. In use of the advanced EVD board, the voltage potential and pore pressure of anode were measured. Moreover, the derived formulas were verified. The result shows that the decrease of electric force gradient had an observable impact on the drainage capability. There was nearly no difference between the energy consumption for the two drainage methods. Although a little less water was discharged, the top drainage method had more advantages, such as high initial drainage velocity, few soil cracks, low anode water content and high soil strength. All of these show that the super soft soil ground could be consolidated quickly in use of the advanced EVD board through the top drainage. The top drainage method could efficiently improve the drainage effect, decrease the energy consumption and speed up the project proceeding.展开更多
Many studies have analyzed the cumulative production performance in the SAGD(steam assisted gravity drainage)process by data-driven models but a study based on these models for a dynamic analysis of a SAGD production ...Many studies have analyzed the cumulative production performance in the SAGD(steam assisted gravity drainage)process by data-driven models but a study based on these models for a dynamic analysis of a SAGD production period is still rare.It is important for engineers to define the production period in a SAGD process as it has a stable and high oil production rate and engineers need to reset operational conditions after the production period starts.In this paper,a series of SAGD models were constructed with selected ranges of reservoir properties and operational conditions.Three SAGD production period parameters,including the start date,end date,and duration,are collected based on the simulated production performances.artificial neural network,extreme gradient boosting,light gradient boosting machine,and catboost were constructed to reveal the hidden relationships between twelve input parameters and three output parameters.The data-driven models were trained,tested,and evaluated.The results showed that compared with the other output parameters,the R^(2) of the end date is the highest and it becomes higher with a larger training data set.The extreme gradient boosting algorithm is a better choice to predict the Start date while the artificial neural network generates better prediction for the other two output parameters.This study shows a significant potential in the use of data-driven models for the SAGD production dynamic analysis.The results also serve to support the utilization of the datadriven models as efficient tools for predicting a SAGD production period.展开更多
基金Project(B15020060)supported by Fundamental Research Funds for the Central Universities,China
文摘As an advanced polymer composites electro-kinetic geosynthetics, the electro-osmotic vertical drainage(EVD) board could drain water quickly and accelerate consolidation process. However, the drainage rate was mainly impacted by the vertical drainage capability. Therefore, vertical drainage capability at the top of EVD board was theoretically analyzed. Basic requirements for drainage at the top of the board were summed up, as well as the formula of anode pore pressure when losing the vertical drainage capability. Meanwhile, a contrast test on the top and bottom drainage capacities was conducted. In use of the advanced EVD board, the voltage potential and pore pressure of anode were measured. Moreover, the derived formulas were verified. The result shows that the decrease of electric force gradient had an observable impact on the drainage capability. There was nearly no difference between the energy consumption for the two drainage methods. Although a little less water was discharged, the top drainage method had more advantages, such as high initial drainage velocity, few soil cracks, low anode water content and high soil strength. All of these show that the super soft soil ground could be consolidated quickly in use of the advanced EVD board through the top drainage. The top drainage method could efficiently improve the drainage effect, decrease the energy consumption and speed up the project proceeding.
基金supported by the NSERC/Energi Simulation and Alberta Innovates Chairs at the University of Calgary.
文摘Many studies have analyzed the cumulative production performance in the SAGD(steam assisted gravity drainage)process by data-driven models but a study based on these models for a dynamic analysis of a SAGD production period is still rare.It is important for engineers to define the production period in a SAGD process as it has a stable and high oil production rate and engineers need to reset operational conditions after the production period starts.In this paper,a series of SAGD models were constructed with selected ranges of reservoir properties and operational conditions.Three SAGD production period parameters,including the start date,end date,and duration,are collected based on the simulated production performances.artificial neural network,extreme gradient boosting,light gradient boosting machine,and catboost were constructed to reveal the hidden relationships between twelve input parameters and three output parameters.The data-driven models were trained,tested,and evaluated.The results showed that compared with the other output parameters,the R^(2) of the end date is the highest and it becomes higher with a larger training data set.The extreme gradient boosting algorithm is a better choice to predict the Start date while the artificial neural network generates better prediction for the other two output parameters.This study shows a significant potential in the use of data-driven models for the SAGD production dynamic analysis.The results also serve to support the utilization of the datadriven models as efficient tools for predicting a SAGD production period.
