Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development.PP prediction is an essential requirement to ensure safe drilling opera...Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development.PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore stability.However,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex geo-processes.To overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical methods.The empirical method for PP prediction is comprised of data pre-processing and model establishment stage.Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372.Gamma-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction.The normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone data.The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development.The dataset is prepared and divided into two parts:Training and Testing.The PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression model.The Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.展开更多
胎儿型肺腺癌(fetal adenocarcinoma of the lung,FLAC)是一种罕见的肺部肿瘤。FLAC分为低级别FLAC(low-grade FLAC,L-FLAC)和高级别FLAC(high-grade FLAC,H-FLAC),两者在临床病理特征、生物学行为和临床结局方面有所不同。大多数H-FLA...胎儿型肺腺癌(fetal adenocarcinoma of the lung,FLAC)是一种罕见的肺部肿瘤。FLAC分为低级别FLAC(low-grade FLAC,L-FLAC)和高级别FLAC(high-grade FLAC,H-FLAC),两者在临床病理特征、生物学行为和临床结局方面有所不同。大多数H-FLAC患者是重度吸烟的中年人。本研究描述了1例罕见的非吸烟年轻男性患者,其最初表现为头顶肿块,最终被诊断为H-FLAC。本文旨在增进对FLAC的了解和认识,提高对该疾病的重视,以防止该疾病漏诊与误诊,加强早期识别、精准诊断,从而推进后续的有效治疗、改善预后。展开更多
文摘Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development.PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore stability.However,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex geo-processes.To overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical methods.The empirical method for PP prediction is comprised of data pre-processing and model establishment stage.Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372.Gamma-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction.The normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone data.The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development.The dataset is prepared and divided into two parts:Training and Testing.The PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression model.The Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.
文摘胎儿型肺腺癌(fetal adenocarcinoma of the lung,FLAC)是一种罕见的肺部肿瘤。FLAC分为低级别FLAC(low-grade FLAC,L-FLAC)和高级别FLAC(high-grade FLAC,H-FLAC),两者在临床病理特征、生物学行为和临床结局方面有所不同。大多数H-FLAC患者是重度吸烟的中年人。本研究描述了1例罕见的非吸烟年轻男性患者,其最初表现为头顶肿块,最终被诊断为H-FLAC。本文旨在增进对FLAC的了解和认识,提高对该疾病的重视,以防止该疾病漏诊与误诊,加强早期识别、精准诊断,从而推进后续的有效治疗、改善预后。
