The connectivity of sandbodies is a key constraint to the exploration effectiveness of Bohai A Oilfield.Conventional connectivity studies often use methods such as seismic attribute fusion,while the development of con...The connectivity of sandbodies is a key constraint to the exploration effectiveness of Bohai A Oilfield.Conventional connectivity studies often use methods such as seismic attribute fusion,while the development of contiguous composite sandbodies in this area makes it challenging to characterize connectivity changes with conventional seismic attributes.Aiming at the above problem in the Bohai A Oilfield,this study proposes a big data analysis method based on the Deep Forest algorithm to predict the sandbody connectivity.Firstly,by compiling the abundant exploration and development sandbodies data in the study area,typical sandbodies with reliable connectivity were selected.Then,sensitive seismic attribute were extracted to obtain training samples.Finally,based on the Deep Forest algorithm,mapping model between attribute combinations and sandbody connectivity was established through machine learning.This method achieves the first quantitative determination of the connectivity for continuous composite sandbodies in the Bohai Oilfield.Compared with conventional connectivity discrimination methods such as high-resolution processing and seismic attribute analysis,this method can combine the sandbody characteristics of the study area in the process of machine learning,and jointly judge connectivity by combining multiple seismic attributes.The study results show that this method has high accuracy and timeliness in predicting connectivity for continuous composite sandbodies.Applied to the Bohai A Oilfield,it successfully identified multiple sandbody connectivity relationships and provided strong support for the subsequent exploration potential assessment and well placement optimization.This method also provides a new idea and method for studying sandbody connectivity under similar complex geological conditions.展开更多
To the Editor:Critically ill patients are always complicated with systematic inflammation causing organ dysfunction,even multiple organ dysfunction syndrome(MODS)or sepsis,which commonly contributes to mortality in in...To the Editor:Critically ill patients are always complicated with systematic inflammation causing organ dysfunction,even multiple organ dysfunction syndrome(MODS)or sepsis,which commonly contributes to mortality in intensive care unit(ICU).It was originally thought that ferritin plays an important role in the hematopoietic system for its iron storage capacity.Recently,it was reported that the raised plasma ferritin is correlated with a poor prognosis of diseases.The level of ferritin could not only reflect disease activity,but also may predict the outcomes.[1]The relationship between plasma ferritin and clinical outcomes of critically ill or sepsis patients remained controversial.展开更多
文摘The connectivity of sandbodies is a key constraint to the exploration effectiveness of Bohai A Oilfield.Conventional connectivity studies often use methods such as seismic attribute fusion,while the development of contiguous composite sandbodies in this area makes it challenging to characterize connectivity changes with conventional seismic attributes.Aiming at the above problem in the Bohai A Oilfield,this study proposes a big data analysis method based on the Deep Forest algorithm to predict the sandbody connectivity.Firstly,by compiling the abundant exploration and development sandbodies data in the study area,typical sandbodies with reliable connectivity were selected.Then,sensitive seismic attribute were extracted to obtain training samples.Finally,based on the Deep Forest algorithm,mapping model between attribute combinations and sandbody connectivity was established through machine learning.This method achieves the first quantitative determination of the connectivity for continuous composite sandbodies in the Bohai Oilfield.Compared with conventional connectivity discrimination methods such as high-resolution processing and seismic attribute analysis,this method can combine the sandbody characteristics of the study area in the process of machine learning,and jointly judge connectivity by combining multiple seismic attributes.The study results show that this method has high accuracy and timeliness in predicting connectivity for continuous composite sandbodies.Applied to the Bohai A Oilfield,it successfully identified multiple sandbody connectivity relationships and provided strong support for the subsequent exploration potential assessment and well placement optimization.This method also provides a new idea and method for studying sandbody connectivity under similar complex geological conditions.
基金Northern Jiangsu People’s Hospital Foundation(Nos. yzucms 201919 and yzucms202001)
文摘To the Editor:Critically ill patients are always complicated with systematic inflammation causing organ dysfunction,even multiple organ dysfunction syndrome(MODS)or sepsis,which commonly contributes to mortality in intensive care unit(ICU).It was originally thought that ferritin plays an important role in the hematopoietic system for its iron storage capacity.Recently,it was reported that the raised plasma ferritin is correlated with a poor prognosis of diseases.The level of ferritin could not only reflect disease activity,but also may predict the outcomes.[1]The relationship between plasma ferritin and clinical outcomes of critically ill or sepsis patients remained controversial.
