Layer regrouping is to divide all the layers into several sets of production series according to the physical properties and recovery percent of layers at high water-cut stage, which is an important technique to impro...Layer regrouping is to divide all the layers into several sets of production series according to the physical properties and recovery percent of layers at high water-cut stage, which is an important technique to improve oil recovery for high water-cut multilayered reservoirs. Dif- ferent regroup scenarios may lead to different production performances. Based on unstable oil-water flow theory, a multilayer commingled reservoir simulator is established by modifying the production split method. Taking into account the differences of layer properties, including per- meability, oil viscosity, and remaining oil saturation, the pseudo flow resistance contrast is proposed to serve as a characteristic index of layer regrouping for high water-cut multilayered reservoirs. The production indices of multi- layered reservoirs with different pseudo flow resistances are predicted with the established model in which the data are taken from the Shengtuo Oilfield. Simulation results show that the pseudo flow resistance contrast should be less than 4 when the layer regrouping is implemented. The K-means clustering method, which is based on the objec- tive function, is used to automatically carry out the layer regrouping process according to pseudo flow resistances. The research result is applied to the IV-VI sand groups of the second member of the Shahejie Formation in the Shengtuo Oilfield, a favorable development performance is obtained, and the oil recovery is enhanced by 6.08 %.展开更多
The rapid changing near source, multi-stream depositional environment of conglomerate reservoirs leads to severe heterogeneity, complex lithology and physical properties, and large changes of oil layer resistivity. Qu...The rapid changing near source, multi-stream depositional environment of conglomerate reservoirs leads to severe heterogeneity, complex lithology and physical properties, and large changes of oil layer resistivity. Quantitative evaluation of water-flooded layers has become an important but difficult focus for secondary development of oilfields. In this paper, based on the analysis of current problems in quantitative evaluation of water-flooded layers, the Kexia Group conglomerate reservoir of the Sixth District in the Karamay Oilfield was studied. Eight types of conglomerate reservoir lithology were identified effectively by a data mining method combined with the data from sealed coring wells, and then a multi-parameter model for quantitative evaluation of the water-flooded layers of the main oil-bearing lithology was developed. Water production rate, oil saturation and oil productivity index were selected as the characteristic parameters for quantitative evaluation of water-flooded layers of conglomerate reservoirs. Finally, quantitative evaluation criteria and identification rules for water-flooded layers of main oil-bearing lithology formed by integration of the three characteristic parameters of water-flooded layer and undisturbed formation resistivity. This method has been used in evaluation of the water-flooded layers of a conglomerate reservoir in the Karamay Oilfield and achieved good results, improving the interpretation accuracy and compliance rate. It will provide technical support for avoiding perforation of high water-bearing layers and for adjustment of developmental programs.展开更多
Experiments of electrical responses of waterflooded layers were carried out on porous,fractured,porous-fractured and composite cores taken from carbonate reservoirs in the Zananor Oilfield,Kazakhstan to find out the e...Experiments of electrical responses of waterflooded layers were carried out on porous,fractured,porous-fractured and composite cores taken from carbonate reservoirs in the Zananor Oilfield,Kazakhstan to find out the effects of injected water salinity on electrical responses of carbonate reservoirs.On the basis of the experimental results and the mathematical model of calculating oil-water relative permeability of porous reservoirs by resistivity and the relative permeability model of two-phase flow in fractured reservoirs,the classification standards of water-flooded layers suitable for carbonate reservoirs with complex pore structure were established.The results show that the salinity of injected water is the main factor affecting the resistivity of carbonate reservoir.When low salinity water(fresh water)is injected,the relationship curve between resistivity and water saturation is U-shaped.When high salinity water(salt water)is injected,the curve is L-shaped.The classification criteria of water-flooded layers for carbonate reservoirs are as follows:(1)In porous reservoirs,the water cut(fw)is less than or equal to 5%in oil layers,5%–20%in weak water-flooded layers,20%–50%in moderately water-flooded layers,and greater than 50%in strong water-flooded layers.(2)For fractured,porous-fractured and composite reservoirs,the oil layers,weakly water-flooded layers,moderately water-flooded layers,and severely water-flooded layers have a water content of less than or equal to 5%,5%and 10%,10%to 50%,and larger than 50%respectively.展开更多
Arrhythmias may lead to sudden cardiac death if not detected and treated in time.A supraventricular premature beat(SPB)and premature ventricular contraction(PVC)are important categories of arrhythmia disease.Recently,...Arrhythmias may lead to sudden cardiac death if not detected and treated in time.A supraventricular premature beat(SPB)and premature ventricular contraction(PVC)are important categories of arrhythmia disease.Recently,deep learning methods have been applied to the PVC/SPB heartbeats detection.However,most researchers have focused on time-domain information of the electrocardiogram and there has been a lack of exploration of the interpretability of the model.In this study,we design an interpretable and accurate PVC/SPB recognition algorithm,called the interpretable multilevel wavelet decomposition deep network(IMWDDN).Wavelet decomposition is introduced into the deep network and the squeeze and excitation(SE)-Residual block is designed for extracting time-domain and frequency-domain features.Additionally,inspired by the idea of residual learning,we construct a novel loss function for the constant updating of the multilevel wavelet decomposition parameters.Finally,the IMWDDN is evaluated on the Third China Physiological Signal Challenge Dataset and the MIT-BIH Arrhythmia database.The comparison results show IMWDDN has better detection performance with 98.51%accuracy and a 93.75%F1-macro on average,and its areas of concern are similar to those of an expert diagnosis to a certain extent.Generally,the IMWDDN has good application value in the clinical screening of PVC/SPB heartbeats.展开更多
基金supported by the Program for Changjiang Scholars and Innovative Research Team in University(IRT1294)the China National Science and Technology Major Projects(Grant No:2016ZX05011)
文摘Layer regrouping is to divide all the layers into several sets of production series according to the physical properties and recovery percent of layers at high water-cut stage, which is an important technique to improve oil recovery for high water-cut multilayered reservoirs. Dif- ferent regroup scenarios may lead to different production performances. Based on unstable oil-water flow theory, a multilayer commingled reservoir simulator is established by modifying the production split method. Taking into account the differences of layer properties, including per- meability, oil viscosity, and remaining oil saturation, the pseudo flow resistance contrast is proposed to serve as a characteristic index of layer regrouping for high water-cut multilayered reservoirs. The production indices of multi- layered reservoirs with different pseudo flow resistances are predicted with the established model in which the data are taken from the Shengtuo Oilfield. Simulation results show that the pseudo flow resistance contrast should be less than 4 when the layer regrouping is implemented. The K-means clustering method, which is based on the objec- tive function, is used to automatically carry out the layer regrouping process according to pseudo flow resistances. The research result is applied to the IV-VI sand groups of the second member of the Shahejie Formation in the Shengtuo Oilfield, a favorable development performance is obtained, and the oil recovery is enhanced by 6.08 %.
文摘The rapid changing near source, multi-stream depositional environment of conglomerate reservoirs leads to severe heterogeneity, complex lithology and physical properties, and large changes of oil layer resistivity. Quantitative evaluation of water-flooded layers has become an important but difficult focus for secondary development of oilfields. In this paper, based on the analysis of current problems in quantitative evaluation of water-flooded layers, the Kexia Group conglomerate reservoir of the Sixth District in the Karamay Oilfield was studied. Eight types of conglomerate reservoir lithology were identified effectively by a data mining method combined with the data from sealed coring wells, and then a multi-parameter model for quantitative evaluation of the water-flooded layers of the main oil-bearing lithology was developed. Water production rate, oil saturation and oil productivity index were selected as the characteristic parameters for quantitative evaluation of water-flooded layers of conglomerate reservoirs. Finally, quantitative evaluation criteria and identification rules for water-flooded layers of main oil-bearing lithology formed by integration of the three characteristic parameters of water-flooded layer and undisturbed formation resistivity. This method has been used in evaluation of the water-flooded layers of a conglomerate reservoir in the Karamay Oilfield and achieved good results, improving the interpretation accuracy and compliance rate. It will provide technical support for avoiding perforation of high water-bearing layers and for adjustment of developmental programs.
基金Supported by the China National Major Science and Technology Project(2017ZX05030-002)the Natural Science Basic Research Plan in Shaanxi Province of China(2020JQ-747)the Fundamental Research Funds for the Central Universities(300102260107)
文摘Experiments of electrical responses of waterflooded layers were carried out on porous,fractured,porous-fractured and composite cores taken from carbonate reservoirs in the Zananor Oilfield,Kazakhstan to find out the effects of injected water salinity on electrical responses of carbonate reservoirs.On the basis of the experimental results and the mathematical model of calculating oil-water relative permeability of porous reservoirs by resistivity and the relative permeability model of two-phase flow in fractured reservoirs,the classification standards of water-flooded layers suitable for carbonate reservoirs with complex pore structure were established.The results show that the salinity of injected water is the main factor affecting the resistivity of carbonate reservoir.When low salinity water(fresh water)is injected,the relationship curve between resistivity and water saturation is U-shaped.When high salinity water(salt water)is injected,the curve is L-shaped.The classification criteria of water-flooded layers for carbonate reservoirs are as follows:(1)In porous reservoirs,the water cut(fw)is less than or equal to 5%in oil layers,5%–20%in weak water-flooded layers,20%–50%in moderately water-flooded layers,and greater than 50%in strong water-flooded layers.(2)For fractured,porous-fractured and composite reservoirs,the oil layers,weakly water-flooded layers,moderately water-flooded layers,and severely water-flooded layers have a water content of less than or equal to 5%,5%and 10%,10%to 50%,and larger than 50%respectively.
基金supported by the National Postdoctoral Program for Innovative Talents(Grant No.BX20230215)China Postdoctoral Science Foundation(Grant No.2023M732219)Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0102)。
文摘Arrhythmias may lead to sudden cardiac death if not detected and treated in time.A supraventricular premature beat(SPB)and premature ventricular contraction(PVC)are important categories of arrhythmia disease.Recently,deep learning methods have been applied to the PVC/SPB heartbeats detection.However,most researchers have focused on time-domain information of the electrocardiogram and there has been a lack of exploration of the interpretability of the model.In this study,we design an interpretable and accurate PVC/SPB recognition algorithm,called the interpretable multilevel wavelet decomposition deep network(IMWDDN).Wavelet decomposition is introduced into the deep network and the squeeze and excitation(SE)-Residual block is designed for extracting time-domain and frequency-domain features.Additionally,inspired by the idea of residual learning,we construct a novel loss function for the constant updating of the multilevel wavelet decomposition parameters.Finally,the IMWDDN is evaluated on the Third China Physiological Signal Challenge Dataset and the MIT-BIH Arrhythmia database.The comparison results show IMWDDN has better detection performance with 98.51%accuracy and a 93.75%F1-macro on average,and its areas of concern are similar to those of an expert diagnosis to a certain extent.Generally,the IMWDDN has good application value in the clinical screening of PVC/SPB heartbeats.
