Based on the study of the distribution of intra-platform shoals and the characteristics of dolomite reservoirs in the Middle Permian Qixia Formation in the Gaoshiti–Moxi area of the Sichuan Basin,SW China,the control...Based on the study of the distribution of intra-platform shoals and the characteristics of dolomite reservoirs in the Middle Permian Qixia Formation in the Gaoshiti–Moxi area of the Sichuan Basin,SW China,the controlling factors of reservoir development were analyzed,and the formation model of“intra-platform shoal thin-layer dolomite reservoir”was established.The Qixia Formation is a regressive cycle from bottom to top,in which the first member(Qi1 Member)develops low-energy open sea microfacies,and the second member(Qi2 Member)evolves into intra-platform shoal and inter-shoal sea with decreases in sea level.The intra-platform shoal is mainly distributed near the top of two secondary shallowing cycles of the Qi2 Member.The most important reservoir rock of the Qixia Formation is thin-layer fractured-vuggy dolomite,followed by vuggy dolomite.The semi-filled saddle dolomite is common in fracture-vug,and intercrystalline pores and residual dissolution pores combined with fractures to form the effective pore-fracture network.Based on the coupling analysis of sedimentary and diagenesis characteristics,the reservoir formation model of“pre-depositional micro-paleogeomorphology controlling shoal,sedimentary shoal controlling dolomite,penecontemporaneous dolomite benefiting preservation of pores,and late hydrothermal action effectively improving reservoir quality”was systematically established.The“first-order high zone”micro-paleogeomorphology before the deposition of the Qixia Formation controlled the development of large area of intra-platform shoals in Gaoshiti area during the deposition of the Qi2 Member.Shoal facies is the basic condition of early dolomitization,and the distribution range of intra-platform shoal and dolomite reservoir is highly consistent.The grain limestone of shoal facies is transformed by two stages of dolomitization.The penecontemporaneous dolomitization is conducive to the preservation of primary pores and secondary dissolved pores.The burial hydrothermal fluid enters the early dolomite body along the fractures associated with the Emeishan basalt event,makes it recrystallized into medium–coarse crystal dolomite.With the intercrystalline pores and the residual vugs after the hydrothermal dissolution along the fractures,the high-quality intra-platform shoal-type thin-layer dolomite reservoirs are formed.The establishment of this reservoir formation model can provide important theoretical support for the sustainable development of Permian gas reservoirs in the Sichuan Basin.展开更多
Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-drive...Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.展开更多
基金Supported by the National Natural Science Foundation of China(42172177)CNPC Scientific Research and Technological Development Project(2021DJ05)PetroChina-Southwest University of Petroleum Innovation Consortium Project(2020CX020000).
文摘Based on the study of the distribution of intra-platform shoals and the characteristics of dolomite reservoirs in the Middle Permian Qixia Formation in the Gaoshiti–Moxi area of the Sichuan Basin,SW China,the controlling factors of reservoir development were analyzed,and the formation model of“intra-platform shoal thin-layer dolomite reservoir”was established.The Qixia Formation is a regressive cycle from bottom to top,in which the first member(Qi1 Member)develops low-energy open sea microfacies,and the second member(Qi2 Member)evolves into intra-platform shoal and inter-shoal sea with decreases in sea level.The intra-platform shoal is mainly distributed near the top of two secondary shallowing cycles of the Qi2 Member.The most important reservoir rock of the Qixia Formation is thin-layer fractured-vuggy dolomite,followed by vuggy dolomite.The semi-filled saddle dolomite is common in fracture-vug,and intercrystalline pores and residual dissolution pores combined with fractures to form the effective pore-fracture network.Based on the coupling analysis of sedimentary and diagenesis characteristics,the reservoir formation model of“pre-depositional micro-paleogeomorphology controlling shoal,sedimentary shoal controlling dolomite,penecontemporaneous dolomite benefiting preservation of pores,and late hydrothermal action effectively improving reservoir quality”was systematically established.The“first-order high zone”micro-paleogeomorphology before the deposition of the Qixia Formation controlled the development of large area of intra-platform shoals in Gaoshiti area during the deposition of the Qi2 Member.Shoal facies is the basic condition of early dolomitization,and the distribution range of intra-platform shoal and dolomite reservoir is highly consistent.The grain limestone of shoal facies is transformed by two stages of dolomitization.The penecontemporaneous dolomitization is conducive to the preservation of primary pores and secondary dissolved pores.The burial hydrothermal fluid enters the early dolomite body along the fractures associated with the Emeishan basalt event,makes it recrystallized into medium–coarse crystal dolomite.With the intercrystalline pores and the residual vugs after the hydrothermal dissolution along the fractures,the high-quality intra-platform shoal-type thin-layer dolomite reservoirs are formed.The establishment of this reservoir formation model can provide important theoretical support for the sustainable development of Permian gas reservoirs in the Sichuan Basin.
基金This work was financially supported by National Natural Science Foundation of China(41972262)Hebei Natural Science Foundation for Excellent Young Scholars(D2020504032)+1 种基金Central Plains Science and technology innovation leader Project(214200510030)Key research and development Project of Henan province(221111321500).
文摘Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.
