In order to improve the effect of water control and oil stabilization during high water cut period, a mathematical model of five point method well group was established with the high water cut well group of an Oilfiel...In order to improve the effect of water control and oil stabilization during high water cut period, a mathematical model of five point method well group was established with the high water cut well group of an Oilfield as the target area, the variation law of water cut and recovery factor of different injection parameters was analyzed, and the optimization research of injection parameters of polymer enhanced foam flooding was carried out. The results show that the higher the injection rate, the lower the water content curve, and the higher the oil recovery rate. As the foam defoamed when encountering oil, when the injection time was earlier than 80% of water cut, the later the injection time was, the better the oil displacement effect would be. When the injection time was later than 80% of water cut, the later the injection time was, the worse the oil displacement effect would be. The larger the injection volume, the lower the water content curve and the higher the recovery rate. After the injection volume exceeded 0.2 PV, the amplitude of changes in water content and recovery rate slowed down. The optimal injection parameters of profile control agent for high water content well group in Oilfield A were: injection rate of 15 m<sup>3</sup>/d, injection timing of 80% water content, and injection volume of 0.2 PV.展开更多
A deep learning method for predicting oil field production at ultra-high water cut stage from the existing oil field production data was presented,and the experimental verification and application effect analysis were...A deep learning method for predicting oil field production at ultra-high water cut stage from the existing oil field production data was presented,and the experimental verification and application effect analysis were carried out.Since the traditional Fully Connected Neural Network(FCNN)is incapable of preserving the correlation of time series data,the Long Short-Term Memory(LSTM)network,which is a kind of Recurrent Neural Network(RNN),was utilized to establish a model for oil field production prediction.By this model,oil field production can be predicted from the relationship between oil production index and its influencing factors and the trend and correlation of oil production over time.Production data of a medium and high permeability sandstone oilfield in China developed by water flooding was used to predict its production at ultra-high water cut stage,and the results were compared with the results from the traditional FCNN and water drive characteristic curves.The LSTM based on deep learning has higher precision,and gives more accurate production prediction for complex time series in oil field production.The LSTM model was used to predict the monthly oil production of another two oil fields.The prediction results are good,which verifies the versatility of the method.展开更多
The oil production predicted by means of the conventional water-drive characteristic curve is typically affected by large deviations with respect to the actual value when the so-called high water-cut stage is entered....The oil production predicted by means of the conventional water-drive characteristic curve is typically affected by large deviations with respect to the actual value when the so-called high water-cut stage is entered.In order to solve this problem,a new characteristic relationship between the relative permeability ratio and the average water saturation is proposed.By comparing the outcomes of different matching methods,it is verified that it can well reflect the variation characteristics of the relative permeability ratio curve.Combining the new formula with a reservoir engineering method,two new formulas are derived for the water flooding characteristic curve in the high water-cut stage.Their practicability is verified by using the production data of Mawangmiao and Xijiakou blocks.The results show that the error between the predicted cumulative oil production and production data of the two new water drive characteristic curves is less than the error between the B-type water drive characteristic curve and the other two water drive characteristic curves.It is concluded that the two new characteristic curves can be used to estimate more accurately the recoverable reserves,the final recovery and to estimate the effects of water flooding.展开更多
文摘In order to improve the effect of water control and oil stabilization during high water cut period, a mathematical model of five point method well group was established with the high water cut well group of an Oilfield as the target area, the variation law of water cut and recovery factor of different injection parameters was analyzed, and the optimization research of injection parameters of polymer enhanced foam flooding was carried out. The results show that the higher the injection rate, the lower the water content curve, and the higher the oil recovery rate. As the foam defoamed when encountering oil, when the injection time was earlier than 80% of water cut, the later the injection time was, the better the oil displacement effect would be. When the injection time was later than 80% of water cut, the later the injection time was, the worse the oil displacement effect would be. The larger the injection volume, the lower the water content curve and the higher the recovery rate. After the injection volume exceeded 0.2 PV, the amplitude of changes in water content and recovery rate slowed down. The optimal injection parameters of profile control agent for high water content well group in Oilfield A were: injection rate of 15 m<sup>3</sup>/d, injection timing of 80% water content, and injection volume of 0.2 PV.
基金Supported by China National Science and Technology Major Project(2016ZX05016-006)
文摘A deep learning method for predicting oil field production at ultra-high water cut stage from the existing oil field production data was presented,and the experimental verification and application effect analysis were carried out.Since the traditional Fully Connected Neural Network(FCNN)is incapable of preserving the correlation of time series data,the Long Short-Term Memory(LSTM)network,which is a kind of Recurrent Neural Network(RNN),was utilized to establish a model for oil field production prediction.By this model,oil field production can be predicted from the relationship between oil production index and its influencing factors and the trend and correlation of oil production over time.Production data of a medium and high permeability sandstone oilfield in China developed by water flooding was used to predict its production at ultra-high water cut stage,and the results were compared with the results from the traditional FCNN and water drive characteristic curves.The LSTM based on deep learning has higher precision,and gives more accurate production prediction for complex time series in oil field production.The LSTM model was used to predict the monthly oil production of another two oil fields.The prediction results are good,which verifies the versatility of the method.
基金It is supported by the National Natural Science Foundation of China(No.51404037)supported by the Scientific and Technological Research Project of Sinopec Jianghan Oilfield Branch Company(No.ZKK0220006).
文摘The oil production predicted by means of the conventional water-drive characteristic curve is typically affected by large deviations with respect to the actual value when the so-called high water-cut stage is entered.In order to solve this problem,a new characteristic relationship between the relative permeability ratio and the average water saturation is proposed.By comparing the outcomes of different matching methods,it is verified that it can well reflect the variation characteristics of the relative permeability ratio curve.Combining the new formula with a reservoir engineering method,two new formulas are derived for the water flooding characteristic curve in the high water-cut stage.Their practicability is verified by using the production data of Mawangmiao and Xijiakou blocks.The results show that the error between the predicted cumulative oil production and production data of the two new water drive characteristic curves is less than the error between the B-type water drive characteristic curve and the other two water drive characteristic curves.It is concluded that the two new characteristic curves can be used to estimate more accurately the recoverable reserves,the final recovery and to estimate the effects of water flooding.
