In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wuton...In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years＇ recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil/water layers by conventional well log interpretation. This paper proposes a methodology called intelligent prediction and identification system （IPIS）. Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related to reservoir fluids have been selected carefully. They are shale content （Vsh）, numbered rock type （RN）, porosity （φ）, permeability （K）, true resistivity （RT） and spontaneous-potential （SP）. Secondly, Vsh, φ and K are predicted from well logs through artificial neural networks （ANNs）. Finally, all the six parameters are input into a neuro-fuzzy inference machine （NFIM） to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system＇s accuracy and effectiveness.展开更多
The targeted reservoir,which is referred as the first member of Cretaceous Qingshankou Formation in Gulong Sag,Songliao Basin,NE China,is characterized by the enrichment of clay and lamellation fractures.Aiming at the...The targeted reservoir,which is referred as the first member of Cretaceous Qingshankou Formation in Gulong Sag,Songliao Basin,NE China,is characterized by the enrichment of clay and lamellation fractures.Aiming at the technical challenge of determining oil saturation of such reservoir,nano-pores were accurately described and located through focused ion beam scanning electron microscopy and quantitative evaluation of minerals by scanning electron microscopy based on Simandoux model,to construct a 4D digital core frame.Electrical parameters of the shale reservoir were determined by finite element simulation,and the oil saturation calculation method suitable for shale was proposed.Comparison between the results from this method with that from real core test and 2D nuclear magnetic log shows that the absolute errors meet the requirements of the current reserve specification in China for clay-rich shale reservoir.Comparison analysis of multiple wells shows that the oil saturation values calculated by this method of several points vertically in single wells and multiple wells on the plane are in agreement with the test results of core samples and the regional deposition pattern,proving the accuracy and applicability of the method model.展开更多
基金financially supported by the National Science and Technology Major Demonstration Project 19 (2011ZX05062-008)
文摘In recent years, as the exploration practices extend into more complicated formations, conventional well log interpretation has often shown its inaccuracy and limitations in identifying hydrocarbons. The Permian Wutonggou Formation hosts typical clastic reservoirs in the Eastern Junggar Basin. The sophisticated lithology characteristics cause complex pore structures and fluid properties. These all finally cause low well testing agreement rate using conventional methods. Eleven years＇ recent statistics show that 12 out of 15 water layers have been incorrectly identified as being oil or oil/water layers by conventional well log interpretation. This paper proposes a methodology called intelligent prediction and identification system （IPIS）. Firstly, parameters reflecting lithological, petrophysical and electrical responses which are greatly related to reservoir fluids have been selected carefully. They are shale content （Vsh）, numbered rock type （RN）, porosity （φ）, permeability （K）, true resistivity （RT） and spontaneous-potential （SP）. Secondly, Vsh, φ and K are predicted from well logs through artificial neural networks （ANNs）. Finally, all the six parameters are input into a neuro-fuzzy inference machine （NFIM） to get fluids identification results. Eighteen new layers of 145.3 m effective thickness were examined by IPIS. There is full agreement with well testing results. This shows the system＇s accuracy and effectiveness.
基金Supported by the PetroChina"Fourteenth Five-Year Plan"Prospective Basic Technology Research Project(2021DJ4002)PetroChina Major Oil and Gas Project(2021ZZ10-01).
文摘The targeted reservoir,which is referred as the first member of Cretaceous Qingshankou Formation in Gulong Sag,Songliao Basin,NE China,is characterized by the enrichment of clay and lamellation fractures.Aiming at the technical challenge of determining oil saturation of such reservoir,nano-pores were accurately described and located through focused ion beam scanning electron microscopy and quantitative evaluation of minerals by scanning electron microscopy based on Simandoux model,to construct a 4D digital core frame.Electrical parameters of the shale reservoir were determined by finite element simulation,and the oil saturation calculation method suitable for shale was proposed.Comparison between the results from this method with that from real core test and 2D nuclear magnetic log shows that the absolute errors meet the requirements of the current reserve specification in China for clay-rich shale reservoir.Comparison analysis of multiple wells shows that the oil saturation values calculated by this method of several points vertically in single wells and multiple wells on the plane are in agreement with the test results of core samples and the regional deposition pattern,proving the accuracy and applicability of the method model.
