In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essen...In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essential for determining the behavior of actual reservoirs.The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data.This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity,which dynamics of the process.One of the practical machine learning(ML)techniques for regression/classification problems is gradient boosting(GB)regression.The GB produces a prediction model as an ensemble of weak prediction models that can be done at each iteration by matching a least-squares base-learner with the current pseudoresiduals.Using a randomization process increases the execution speed and accuracy of GB.Hence in this study,we developed a stochastic regression model of gradient boosting(SGB)to forecast oil recovery.Different nondimensional time-scales have been used to generate data to be used with machine learning techniques.The SGB method has been found to be the best machine learning technique for predicting the non-dimensional time-scale,which depends on oil/rock properties.展开更多
文摘In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essential for determining the behavior of actual reservoirs.The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data.This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity,which dynamics of the process.One of the practical machine learning(ML)techniques for regression/classification problems is gradient boosting(GB)regression.The GB produces a prediction model as an ensemble of weak prediction models that can be done at each iteration by matching a least-squares base-learner with the current pseudoresiduals.Using a randomization process increases the execution speed and accuracy of GB.Hence in this study,we developed a stochastic regression model of gradient boosting(SGB)to forecast oil recovery.Different nondimensional time-scales have been used to generate data to be used with machine learning techniques.The SGB method has been found to be the best machine learning technique for predicting the non-dimensional time-scale,which depends on oil/rock properties.
