The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the o...The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the oilfield at present since pre-stack inversion is always limited by poor seismic data quality and insufficient logging data.In this paper,based on amplitude preserved seismic data processing and rock-physics analysis,pre-stack inversion is employed to predict the caved carbonate reservoir in TZ45 area by seriously controlling the quality of inversion procedures.These procedures mainly include angle-gather conversion,partial stack,wavelet estimation,low-frequency model building and inversion residual analysis.The amplitude-preserved data processing method can achieve high quality data based on the principle that they are very consistent with the synthetics.Besides,the foundation of pre-stack inversion and reservoir prediction criterion can be established by the connection between reservoir property and seismic reflection through rock-physics analysis.Finally,the inversion result is consistent with drilling wells in most cases.It is concluded that integrated with amplitude-preserved processing and rock-physics,pre-stack inversion can be effectively applied in the caved carbonate reservoir prediction.展开更多
An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,mo...An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.展开更多
Considering the fluid flow non-darcy characteristics in fracture-vug carbonate reservoirs, a new multi-scale conduit flow model production prediction method for fracture-vug carbonate reservoirs was presented using im...Considering the fluid flow non-darcy characteristics in fracture-vug carbonate reservoirs, a new multi-scale conduit flow model production prediction method for fracture-vug carbonate reservoirs was presented using image segmentation technique of electric imaging logging data. Firstly, based on Hagen-Poiseuille's law of incompressible fluid flow and the different cross-sectional areas in single fractures and vugs in carbonate reservoirs, a multi-scale conduit flow model for fracture-vug carbonate reservoir was established, and a multi-scale conduit radial fluid flow equation was deduced. Then, conduit flow production index was introduced. The conduit flow production index was calculated using fracture-vug area extracted from the result of electrical image segmentation. Finally, production prediction of fracture-vug carbonate reservoir was realized by using electric imaging logging data. The method has been applied to Ordovician fracture-vug carbonate reservoirs in the Tabei area, and the predicted results are in good agreement with the oil testing data.展开更多
By using core, thin section, well logging, seismic, well testing and other data, the reservoir grading evaluation parameters were selected, the classification criterion considering multiple factors for carbonate reser...By using core, thin section, well logging, seismic, well testing and other data, the reservoir grading evaluation parameters were selected, the classification criterion considering multiple factors for carbonate reservoirs in this area were established, and the main factors affecting the development of high quality reservoir were determined. By employing Formation MicroScanner Image(FMI) logging fracture-cavity recognition technology and reservoir seismic waveform classification technology, the spatial distribution of reservoirs of all grades were predicted. On the basis of identifying four types of reservoir space developed in the study area by mercury injection experiment, a classification criterion was established using four reservoir grading evaluation parameters, median throat radius, effective porosity and effective permeability of fracture-cavity development zone, relationship between fracture and dissolution pore development and assemblage, and the reservoirs in the study area were classified into grade I high quality reservoir of fracture and cavity type, grade II average reservoir of fracture and porosity type, grade Ⅲ poor reservoir of intergranular pore type. Based on the three main factors controlling the development of high quality reservoir, structural location, sedimentary facies and epigenesis, the distribution of the 3 grades reservoirs in each well area and formation were predicted using geophysical response and percolation characteristics. Follow-up drilling has confirmed that the classification evaluation standard and prediction methods established are effective.展开更多
基金supported by National Basic Research Program(2006CB202304)of Chinaco-supported by the National Basic Research Program of China(Grant No.2011CB201103)the National Science and Technology Major Project of China(Grant No.2011ZX05004003)
文摘The major storage space types in the carbonate reservoir in the Ordovician in the TZ45 area are secondary dissolution caves.For the prediction of caved carbonate reservoir,post-stack methods are commonly used in the oilfield at present since pre-stack inversion is always limited by poor seismic data quality and insufficient logging data.In this paper,based on amplitude preserved seismic data processing and rock-physics analysis,pre-stack inversion is employed to predict the caved carbonate reservoir in TZ45 area by seriously controlling the quality of inversion procedures.These procedures mainly include angle-gather conversion,partial stack,wavelet estimation,low-frequency model building and inversion residual analysis.The amplitude-preserved data processing method can achieve high quality data based on the principle that they are very consistent with the synthetics.Besides,the foundation of pre-stack inversion and reservoir prediction criterion can be established by the connection between reservoir property and seismic reflection through rock-physics analysis.Finally,the inversion result is consistent with drilling wells in most cases.It is concluded that integrated with amplitude-preserved processing and rock-physics,pre-stack inversion can be effectively applied in the caved carbonate reservoir prediction.
基金Supported by the China Youth Program of National Natural Science Foundation(42002134)The 14th Special Support Program of China Postdoctoral Science Foundation(2021T140735).
文摘An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single-well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.
基金Supported by the China National Science and Technology Major Project(2011ZX05020-008)
文摘Considering the fluid flow non-darcy characteristics in fracture-vug carbonate reservoirs, a new multi-scale conduit flow model production prediction method for fracture-vug carbonate reservoirs was presented using image segmentation technique of electric imaging logging data. Firstly, based on Hagen-Poiseuille's law of incompressible fluid flow and the different cross-sectional areas in single fractures and vugs in carbonate reservoirs, a multi-scale conduit flow model for fracture-vug carbonate reservoir was established, and a multi-scale conduit radial fluid flow equation was deduced. Then, conduit flow production index was introduced. The conduit flow production index was calculated using fracture-vug area extracted from the result of electrical image segmentation. Finally, production prediction of fracture-vug carbonate reservoir was realized by using electric imaging logging data. The method has been applied to Ordovician fracture-vug carbonate reservoirs in the Tabei area, and the predicted results are in good agreement with the oil testing data.
基金Supported by CNPC Science and Technology Major Project(2016ZX052,2016ZX05015-003)
文摘By using core, thin section, well logging, seismic, well testing and other data, the reservoir grading evaluation parameters were selected, the classification criterion considering multiple factors for carbonate reservoirs in this area were established, and the main factors affecting the development of high quality reservoir were determined. By employing Formation MicroScanner Image(FMI) logging fracture-cavity recognition technology and reservoir seismic waveform classification technology, the spatial distribution of reservoirs of all grades were predicted. On the basis of identifying four types of reservoir space developed in the study area by mercury injection experiment, a classification criterion was established using four reservoir grading evaluation parameters, median throat radius, effective porosity and effective permeability of fracture-cavity development zone, relationship between fracture and dissolution pore development and assemblage, and the reservoirs in the study area were classified into grade I high quality reservoir of fracture and cavity type, grade II average reservoir of fracture and porosity type, grade Ⅲ poor reservoir of intergranular pore type. Based on the three main factors controlling the development of high quality reservoir, structural location, sedimentary facies and epigenesis, the distribution of the 3 grades reservoirs in each well area and formation were predicted using geophysical response and percolation characteristics. Follow-up drilling has confirmed that the classification evaluation standard and prediction methods established are effective.
