针对注水井分层注水量诊断技术难题,提出基于分布式光纤温度传感(Distributed Temperature Sensing,DTS)的注水井吸水剖面解释方法。建立考虑微量热效应的注水井温度剖面预测模型,模拟分析注水量、注水时间、储层导热系数等7个因素对温...针对注水井分层注水量诊断技术难题,提出基于分布式光纤温度传感(Distributed Temperature Sensing,DTS)的注水井吸水剖面解释方法。建立考虑微量热效应的注水井温度剖面预测模型,模拟分析注水量、注水时间、储层导热系数等7个因素对温度剖面的影响规律。通过正交试验模拟分析,确定不同因素对注水井温度剖面的影响程度从强到弱分别为注入水温度、注水时间、注水量、井筒半径、储层导热系数、井筒倾斜角度、注水层渗透率,明确影响注水井温度剖面的主控因素为注入水温度、注水时间和注入量。采用模拟退火(Simulated Annealing,SA)算法建立注水井DTS数据反演模型,对一口注水井现场实测DTS数据进行反演,获得较为准确的吸水剖面,单层最大吸水量误差百分比14.25%,平均误差11.09%,验证该反演方法的可靠性。通过DTS数据反演可以实现注水井吸水剖面定量解释,为注水效果评价提供直接依据。展开更多
BACKGROUND Hypothermia during laparoscopic surgery in patients with multiple trauma is a significant concern owing to its potential complications.Machine learning models offer a promising approach to predict the occur...BACKGROUND Hypothermia during laparoscopic surgery in patients with multiple trauma is a significant concern owing to its potential complications.Machine learning models offer a promising approach to predict the occurrence of intraoperative hypothermia.AIM To investigate the value of machine learning model to predict hypothermia during laparoscopic surgery in patients with multiple trauma.METHODS This retrospective study enrolled 220 patients who were admitted with multiple injuries between June 2018 and December 2023.Of these,154 patients were allocated to a training set and the remaining 66 were allocated to a validation set in a 7:3 ratio.In the training set,53 cases experienced intraoperative hypothermia and 101 did not.Logistic regression analysis was used to construct a predictive model of intraoperative hypothermia in patients with polytrauma undergoing laparoscopic surgery.The area under the curve(AUC),sensitivity,and specificity were calculated.RESULTS Comparison of the hypothermia and non-hypothermia groups found significant differences in sex,age,baseline temperature,intraoperative temperature,duration of anesthesia,duration of surgery,intraoperative fluid infusion,crystalloid infusion,colloid infusion,and pneumoperitoneum volume(P<0.05).Differences between other characteristics were not significant(P>0.05).The results of the logistic regression analysis showed that age,baseline temperature,intraoperative temperature,duration of anesthesia,and duration of surgery were independent influencing factors for intraoperative hypothermia during laparoscopic surgery(P<0.05).Calibration curve analysis showed good consistency between the predicted occurrence of intraoperative hypothermia and the actual occurrence(P>0.05).The predictive model had AUCs of 0.850 and 0.829 for the training and validation sets,respectively.CONCLUSION Machine learning effectively predicted intraoperative hypothermia in polytrauma patients undergoing laparoscopic surgery,which improved surgical safety and patient recovery.展开更多
文摘针对Oligo(d T)亲和层析介质的吸附性能,以poly(A)为模型分子,考察了4种Oligo(d T)亲和层析介质的静态吸附平衡、吸附动力学和动态结合载量(DBC),探讨了载量影响相关机制。结果表明,4种介质的合适吸附条件均为0.6 mol·L-1Na Cl、p H=6~7;Monomix d T20静态吸附容量最大,且poly(A)能扩散至介质微球深层孔内,而Poros Oligo(d T)25、Praesto Jetted (d T)25和Nano Gel d T20等3种介质中poly(A)均主要为表层吸附、静态吸附容量稍低;对于DBC,Nano Gel d T20和Monomix d T20的10%穿透的DBC较高,而Poros Oligo (d T)25和Praesto Jetted (d T)25相对略低。经分析,影响载量的主要因素包含基质种类、微球孔径、配基密度、间隔臂和配基长度等。对于基质种类,聚苯乙烯基质可能孔道结构较为特别。对于微球孔径,应针对不同大小的m RNA分子定制不同孔径的微球,以平衡传质阻力与可及吸附表面积之间的矛盾,从而增大DBC。
文摘针对注水井分层注水量诊断技术难题,提出基于分布式光纤温度传感(Distributed Temperature Sensing,DTS)的注水井吸水剖面解释方法。建立考虑微量热效应的注水井温度剖面预测模型,模拟分析注水量、注水时间、储层导热系数等7个因素对温度剖面的影响规律。通过正交试验模拟分析,确定不同因素对注水井温度剖面的影响程度从强到弱分别为注入水温度、注水时间、注水量、井筒半径、储层导热系数、井筒倾斜角度、注水层渗透率,明确影响注水井温度剖面的主控因素为注入水温度、注水时间和注入量。采用模拟退火(Simulated Annealing,SA)算法建立注水井DTS数据反演模型,对一口注水井现场实测DTS数据进行反演,获得较为准确的吸水剖面,单层最大吸水量误差百分比14.25%,平均误差11.09%,验证该反演方法的可靠性。通过DTS数据反演可以实现注水井吸水剖面定量解释,为注水效果评价提供直接依据。
文摘BACKGROUND Hypothermia during laparoscopic surgery in patients with multiple trauma is a significant concern owing to its potential complications.Machine learning models offer a promising approach to predict the occurrence of intraoperative hypothermia.AIM To investigate the value of machine learning model to predict hypothermia during laparoscopic surgery in patients with multiple trauma.METHODS This retrospective study enrolled 220 patients who were admitted with multiple injuries between June 2018 and December 2023.Of these,154 patients were allocated to a training set and the remaining 66 were allocated to a validation set in a 7:3 ratio.In the training set,53 cases experienced intraoperative hypothermia and 101 did not.Logistic regression analysis was used to construct a predictive model of intraoperative hypothermia in patients with polytrauma undergoing laparoscopic surgery.The area under the curve(AUC),sensitivity,and specificity were calculated.RESULTS Comparison of the hypothermia and non-hypothermia groups found significant differences in sex,age,baseline temperature,intraoperative temperature,duration of anesthesia,duration of surgery,intraoperative fluid infusion,crystalloid infusion,colloid infusion,and pneumoperitoneum volume(P<0.05).Differences between other characteristics were not significant(P>0.05).The results of the logistic regression analysis showed that age,baseline temperature,intraoperative temperature,duration of anesthesia,and duration of surgery were independent influencing factors for intraoperative hypothermia during laparoscopic surgery(P<0.05).Calibration curve analysis showed good consistency between the predicted occurrence of intraoperative hypothermia and the actual occurrence(P>0.05).The predictive model had AUCs of 0.850 and 0.829 for the training and validation sets,respectively.CONCLUSION Machine learning effectively predicted intraoperative hypothermia in polytrauma patients undergoing laparoscopic surgery,which improved surgical safety and patient recovery.
