The paleo-temperature(Th)data from fluid inclusions are utilized for thermal history modelling using PetroMod software.Generally,bottom hole temperature(BHT)and vitrinite reflectance(Ro)measurements are widely used in...The paleo-temperature(Th)data from fluid inclusions are utilized for thermal history modelling using PetroMod software.Generally,bottom hole temperature(BHT)and vitrinite reflectance(Ro)measurements are widely used in petroleum system modelling(PSM)in the oil industry for calibration purposes.Th representing the minimum temperature of fluid entrapment estimated from fluid-inclusion study provides extra support to build the thermal models for PSM.Fluid inclusion parameters along with Rock-Eval pyrolysis analysis have been used to predict the maturity of oil in terms of API gravity as well as the maturity of source rocks respectively.Two exploratory wells RV-1(Mumbai Offshore Basin)and KK4C-A-1(Kerala-Konkan Offshore Basin),India were examined and the T_(h)from most of the fluid inclusions of wells RV-1 and KK4C-A-1 fell in the oil window range of 60e140℃suggesting thermal conditions favourable for oil generation in both of the wells.T_(h)of coeval aqueous inclusions along with the Hydrocarbon Fluid inclusions(HCFIs)was used to calibrate PSM.Vital parameters show that source rocks of well RV-1 are mature and that of well KK4C-A-1 are immature.Two sets of PSM are created in terms of generation and expulsion for the dry wells RV-1 and KK4C-A-1 and calibrated each well using fluid inclusion Th and BHT.From the fluid inclusion analysis method,it is evident that hydrocarbon generation happened in both wells and the paleo-temperature indicates that the formations of both wells were subjected to temperatures in the oil window range,even though it was designated as dry wells in the present scenario.The present study highlights the application of fluid inclusion paleo-temperature(Th)during calibration instead of commonly used methods.We could obtain desirable and accurate data output from PSM using T_(h) calibration.展开更多
考虑电池单体老化差异所致的电池组不一致性,针对串联电池组荷电状态(state of charge,SOC)、容量估计问题,提出一种基于自回归等效电路模型(autoregression equivalent circuit model,AR-ECM)的平均差异模型(mean-difference model,MDM...考虑电池单体老化差异所致的电池组不一致性,针对串联电池组荷电状态(state of charge,SOC)、容量估计问题,提出一种基于自回归等效电路模型(autoregression equivalent circuit model,AR-ECM)的平均差异模型(mean-difference model,MDM)。基于此模型,提出串联电池组SOC、容量多尺度联合估计算法。该算法由2个部分组成,一是基于AR-ECM的MDM及差异化模型参数辨识策略:条件辨识策略和定频分组辨识策略;二是基于多时间尺度H无穷滤波(multi-timescale H infinity filter,Mts-HIF)的电池组SOC、容量联合估计算法。通过将所提出MDM中的自回归平均模型(autoregression mean model,AR-MM)与传统MDM中的n阶RC平均模型(nRC mean model,nRC-MM)比较,结果表明所提出的AR-MM在复杂运行工况下具有更优的动态跟随性能。依据最小化信息量准则(akaike information criterion,AIC),AR-MM具有更优的复杂度与精度的权衡。通过与基于多时间尺度扩展卡尔曼滤波(multi-timescale extended Kalman filter,Mts-EKF)联合状态估计算法比较,结果表明所提出的Mts-HIF状态估计算法具有更优的鲁棒性、精度和收敛速度。展开更多
文摘The paleo-temperature(Th)data from fluid inclusions are utilized for thermal history modelling using PetroMod software.Generally,bottom hole temperature(BHT)and vitrinite reflectance(Ro)measurements are widely used in petroleum system modelling(PSM)in the oil industry for calibration purposes.Th representing the minimum temperature of fluid entrapment estimated from fluid-inclusion study provides extra support to build the thermal models for PSM.Fluid inclusion parameters along with Rock-Eval pyrolysis analysis have been used to predict the maturity of oil in terms of API gravity as well as the maturity of source rocks respectively.Two exploratory wells RV-1(Mumbai Offshore Basin)and KK4C-A-1(Kerala-Konkan Offshore Basin),India were examined and the T_(h)from most of the fluid inclusions of wells RV-1 and KK4C-A-1 fell in the oil window range of 60e140℃suggesting thermal conditions favourable for oil generation in both of the wells.T_(h)of coeval aqueous inclusions along with the Hydrocarbon Fluid inclusions(HCFIs)was used to calibrate PSM.Vital parameters show that source rocks of well RV-1 are mature and that of well KK4C-A-1 are immature.Two sets of PSM are created in terms of generation and expulsion for the dry wells RV-1 and KK4C-A-1 and calibrated each well using fluid inclusion Th and BHT.From the fluid inclusion analysis method,it is evident that hydrocarbon generation happened in both wells and the paleo-temperature indicates that the formations of both wells were subjected to temperatures in the oil window range,even though it was designated as dry wells in the present scenario.The present study highlights the application of fluid inclusion paleo-temperature(Th)during calibration instead of commonly used methods.We could obtain desirable and accurate data output from PSM using T_(h) calibration.
文摘考虑电池单体老化差异所致的电池组不一致性,针对串联电池组荷电状态(state of charge,SOC)、容量估计问题,提出一种基于自回归等效电路模型(autoregression equivalent circuit model,AR-ECM)的平均差异模型(mean-difference model,MDM)。基于此模型,提出串联电池组SOC、容量多尺度联合估计算法。该算法由2个部分组成,一是基于AR-ECM的MDM及差异化模型参数辨识策略:条件辨识策略和定频分组辨识策略;二是基于多时间尺度H无穷滤波(multi-timescale H infinity filter,Mts-HIF)的电池组SOC、容量联合估计算法。通过将所提出MDM中的自回归平均模型(autoregression mean model,AR-MM)与传统MDM中的n阶RC平均模型(nRC mean model,nRC-MM)比较,结果表明所提出的AR-MM在复杂运行工况下具有更优的动态跟随性能。依据最小化信息量准则(akaike information criterion,AIC),AR-MM具有更优的复杂度与精度的权衡。通过与基于多时间尺度扩展卡尔曼滤波(multi-timescale extended Kalman filter,Mts-EKF)联合状态估计算法比较,结果表明所提出的Mts-HIF状态估计算法具有更优的鲁棒性、精度和收敛速度。
