Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts.In periods of significant price drops,companies may consider exte...Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts.In periods of significant price drops,companies may consider extended duration of well shut-ins(i.e.temporarily stopping oil production)for economic reasons.For example,prices during the early days of the Covid-19 pandemic forced operators to consider shutting in all or some of their active wells.In the case of partial shut-in,selection of candidate wells may evolve as a challenging decision problem considering the uncertainties involved.In this study,a mature oil field with a long(50+years)production history with 170+wells is considered.Reservoirs with similar conditions face many challenges related to economic sustainability such as frequent maintenance requirements and low production rates.We aimed to solve this decision-making problem through unsupervised machine learning.Average reservoir characteristics at well locations,well production performance statistics and well locations are used as potential features that could characterize similarities and differences among wells.While reservoir characteristics are measured at well locations for the purpose of describing the subsurface reservoir,well performance consists of volumetric rates and pressures,which are frequently measured during oil production.After a multivariate data analysis that explored correlations among parameters,clustering algorithms were used to identify groups of wells that are similar with respect to aforementioned features.Using the field’s reservoir simulation model,scenarios of shutting in different groups of wells were simulated.Forecasted reservoir performance for three years was used for economic evaluation that assumed an oil price drop to$30/bbl for 6,12 or 18 months.Results of economic analysis were analyzed to identify which group(s)of wells should have been shut-in by also considering the sensitivity to different price levels.It was observed that wells can be characterized in the 3-cluster case as low,medium and high performance wells.Analyzing the forecasting scenarios showed that shutting in all or high-and medium-performance wells altogether results in better economic outcomes.The results were most sensitive to the number of active wells and the oil price during the high-price period.This study demonstrated the effectiveness of unsupervised machine learning in well classification for operational decision making purposes.Operating companies may use this approach for improved decision making to select wells for extended shut-in during low oil-price periods.This approach would lead to cost savings especially in mature fields with low-profit margins.展开更多
Extended 17α(H),21β(H)-hopanes and three series of 8,14-secohopanes up to C_(40),including 8α(H),14α(H),17α(H),21β(H)-,8α(H),14α(H),17β(H),21α(H)-and 8α(H),14β(H),17β(H),21α(H)-,were detected by GC-MS-MS...Extended 17α(H),21β(H)-hopanes and three series of 8,14-secohopanes up to C_(40),including 8α(H),14α(H),17α(H),21β(H)-,8α(H),14α(H),17β(H),21α(H)-and 8α(H),14β(H),17β(H),21α(H)-,were detected by GC-MS-MS method in the branched/cyclic hydrocarbon fractions of some unbiodegraded marine oils from the Tazhong uplift in the Tarim Basin,NW China.The coexistence of extended hopanes and 8,14-secohopanes up to C_(40)in unbiodegraded oils suggests that they are primary and independent on biodegradation.The similarity of distribution and composition for extended hopanes and 8,14-secohopanes up to C_(40)in unbiodegraded oils proposes that they could be derived from a similar biological precursor.However,an abrupt decrease up to 3-5 times in the relative abundance from C_(35)to C_(36)in C_(31-40)extended hopanes and extended 8,14-secohopanes suggests that C_(31-35)and C_(36-40)extended hopanes and extended 8,14-secohopanes should have their own biological precursor.The known C35bacteriohopanetetrol should be biological precursor of C_(31-35)extended hopanes and 8,14-secohopanes,but an unknown C_(40)functionalized hopanoid could be biological precursor of C_(36-40)extended hopanes and 8,14-secohopanes.More attention should be paid to their potential roles in oil-source correlation for severely biodegraded oils based on their widespread occurrence in various source rocks,unbiodegraded and severely biodegraded oils.展开更多
埕北B平台上的大型原油储罐设计建造于20世纪80年代,设计规范采用API 650(第7版),材质为JIS G 3101—1976标准的SS41,原设计使用寿命为15年。由于罐壁局部腐蚀深度超过腐蚀余量,需要通过对比设计建造新旧版本规范、国内国外标准、强度核...埕北B平台上的大型原油储罐设计建造于20世纪80年代,设计规范采用API 650(第7版),材质为JIS G 3101—1976标准的SS41,原设计使用寿命为15年。由于罐壁局部腐蚀深度超过腐蚀余量,需要通过对比设计建造新旧版本规范、国内国外标准、强度核算,完成腐蚀后的安全状态评估,采用门形切除逐块更换方案,通过控制焊接整体变形量完成缺陷改造,延长其使用寿命。展开更多
基金support from research grants MGA-2021-42991 and MYL-2022-43726,funded by Istanbul Technical University-Scientific Research Projects,Turkey.Thissupportis gratefully acknowledged.
文摘Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts.In periods of significant price drops,companies may consider extended duration of well shut-ins(i.e.temporarily stopping oil production)for economic reasons.For example,prices during the early days of the Covid-19 pandemic forced operators to consider shutting in all or some of their active wells.In the case of partial shut-in,selection of candidate wells may evolve as a challenging decision problem considering the uncertainties involved.In this study,a mature oil field with a long(50+years)production history with 170+wells is considered.Reservoirs with similar conditions face many challenges related to economic sustainability such as frequent maintenance requirements and low production rates.We aimed to solve this decision-making problem through unsupervised machine learning.Average reservoir characteristics at well locations,well production performance statistics and well locations are used as potential features that could characterize similarities and differences among wells.While reservoir characteristics are measured at well locations for the purpose of describing the subsurface reservoir,well performance consists of volumetric rates and pressures,which are frequently measured during oil production.After a multivariate data analysis that explored correlations among parameters,clustering algorithms were used to identify groups of wells that are similar with respect to aforementioned features.Using the field’s reservoir simulation model,scenarios of shutting in different groups of wells were simulated.Forecasted reservoir performance for three years was used for economic evaluation that assumed an oil price drop to$30/bbl for 6,12 or 18 months.Results of economic analysis were analyzed to identify which group(s)of wells should have been shut-in by also considering the sensitivity to different price levels.It was observed that wells can be characterized in the 3-cluster case as low,medium and high performance wells.Analyzing the forecasting scenarios showed that shutting in all or high-and medium-performance wells altogether results in better economic outcomes.The results were most sensitive to the number of active wells and the oil price during the high-price period.This study demonstrated the effectiveness of unsupervised machine learning in well classification for operational decision making purposes.Operating companies may use this approach for improved decision making to select wells for extended shut-in during low oil-price periods.This approach would lead to cost savings especially in mature fields with low-profit margins.
基金financially supported by the National Natural Science Foundation of China(Grant No.41772119 and 41272169)
文摘Extended 17α(H),21β(H)-hopanes and three series of 8,14-secohopanes up to C_(40),including 8α(H),14α(H),17α(H),21β(H)-,8α(H),14α(H),17β(H),21α(H)-and 8α(H),14β(H),17β(H),21α(H)-,were detected by GC-MS-MS method in the branched/cyclic hydrocarbon fractions of some unbiodegraded marine oils from the Tazhong uplift in the Tarim Basin,NW China.The coexistence of extended hopanes and 8,14-secohopanes up to C_(40)in unbiodegraded oils suggests that they are primary and independent on biodegradation.The similarity of distribution and composition for extended hopanes and 8,14-secohopanes up to C_(40)in unbiodegraded oils proposes that they could be derived from a similar biological precursor.However,an abrupt decrease up to 3-5 times in the relative abundance from C_(35)to C_(36)in C_(31-40)extended hopanes and extended 8,14-secohopanes suggests that C_(31-35)and C_(36-40)extended hopanes and extended 8,14-secohopanes should have their own biological precursor.The known C35bacteriohopanetetrol should be biological precursor of C_(31-35)extended hopanes and 8,14-secohopanes,but an unknown C_(40)functionalized hopanoid could be biological precursor of C_(36-40)extended hopanes and 8,14-secohopanes.More attention should be paid to their potential roles in oil-source correlation for severely biodegraded oils based on their widespread occurrence in various source rocks,unbiodegraded and severely biodegraded oils.
文摘埕北B平台上的大型原油储罐设计建造于20世纪80年代,设计规范采用API 650(第7版),材质为JIS G 3101—1976标准的SS41,原设计使用寿命为15年。由于罐壁局部腐蚀深度超过腐蚀余量,需要通过对比设计建造新旧版本规范、国内国外标准、强度核算,完成腐蚀后的安全状态评估,采用门形切除逐块更换方案,通过控制焊接整体变形量完成缺陷改造,延长其使用寿命。
