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
伊平屋北部热液区(Iheya North hydrothermal field)位于冲绳海槽中部地区。综合大洋钻探计划(IODP)331航次于2010年9月1日至10月4日在该区钻探了5个站位(C0013～C0017):C0016站位位于North Big Chimney(NBC)地区活跃的热液烟囱和硫化...伊平屋北部热液区(Iheya North hydrothermal field)位于冲绳海槽中部地区。综合大洋钻探计划(IODP)331航次于2010年9月1日至10月4日在该区钻探了5个站位(C0013～C0017):C0016站位位于North Big Chimney(NBC)地区活跃的热液烟囱和硫化物—硫酸盐丘状体上;在C0013、C0014、C0015、C0016站位发现了异常高热流值;在热液补给区C0017站位,实现最大钻探深度达到海底下151 m。在活跃的丘状热液喷口处的C0016站位,尽管取芯率只有4.7%,但首次在现代海底获得黑矿型(Kuroko-type)、富闪锌矿的黑色矿石样品。其他4个站位岩芯主要为具有不同热液蚀变和矿化程度(沸石相到绿片岩相)的互层状半深海和火山碎屑沉积物,及火山角砾和浮岩砾屑。钻孔中不规则的地温梯度剖面变化揭示出地下流体的横向运移趋势。现场数据表明,岩芯孔隙水和气体组成在垂向和横向上变化较大。海底作用主要包括通过相态分离而形成高盐水和富气体的流体,矿物蚀变释放Ca而吸附Mg和Na,高温下K释放而低温吸收,硬石膏形成,有机质的微生物氧化和甲烷利用硫酸盐的厌氧氧化,微生物作用下甲烷形成等。船上研究未证实研究区存在活跃的深部生物圈,细胞丰度明显低于以前的ODP/IODP在陆架边缘的钻探站位。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.
文摘伊平屋北部热液区(Iheya North hydrothermal field)位于冲绳海槽中部地区。综合大洋钻探计划(IODP)331航次于2010年9月1日至10月4日在该区钻探了5个站位(C0013～C0017):C0016站位位于North Big Chimney(NBC)地区活跃的热液烟囱和硫化物—硫酸盐丘状体上;在C0013、C0014、C0015、C0016站位发现了异常高热流值;在热液补给区C0017站位,实现最大钻探深度达到海底下151 m。在活跃的丘状热液喷口处的C0016站位,尽管取芯率只有4.7%,但首次在现代海底获得黑矿型(Kuroko-type)、富闪锌矿的黑色矿石样品。其他4个站位岩芯主要为具有不同热液蚀变和矿化程度(沸石相到绿片岩相)的互层状半深海和火山碎屑沉积物,及火山角砾和浮岩砾屑。钻孔中不规则的地温梯度剖面变化揭示出地下流体的横向运移趋势。现场数据表明,岩芯孔隙水和气体组成在垂向和横向上变化较大。海底作用主要包括通过相态分离而形成高盐水和富气体的流体,矿物蚀变释放Ca而吸附Mg和Na,高温下K释放而低温吸收,硬石膏形成,有机质的微生物氧化和甲烷利用硫酸盐的厌氧氧化,微生物作用下甲烷形成等。船上研究未证实研究区存在活跃的深部生物圈,细胞丰度明显低于以前的ODP/IODP在陆架边缘的钻探站位。
