The conventional methods of edge detection can roughly delineate edge position of geological bodies,but there are still some problems such as low detection accuracy and being susceptible to noise interference.In this ...The conventional methods of edge detection can roughly delineate edge position of geological bodies,but there are still some problems such as low detection accuracy and being susceptible to noise interference.In this paper,three image processing methods,Canny,Lo G and Sobel operators are briefly introduced,and applied to edge detection to determine the edge of geological bodies.Furthermore,model data is built to analyze the edge detection ability of this image processing methods,and compare with conventional methods.Combined with gravity anomaly of Sichuan basin and magnetic anomaly of Zhurihe area,the detection effect of image processing methods is further verified in real data.The results show that image processing methods can be applied to effectively identify the edge of geological bodies.Moreover,when both positive and negative anomalies exist and noise is abundant,fake edge can be avoided and edge division is clearer,and satisfactory results of edge detection are obtained.展开更多
Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventi...Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.展开更多
Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to co...Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to core limitations,using image log is considered as the best method.This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin,SW Iran,in order to evaluate natural fractures,porosity system,permeability profile and heterogeneity index and accordingly compare the results with core and well data.The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters,when there is no core available for a well.Based on the results from formation micro-imager(FMI)and electrical micro-imager(EMI),Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures.Furthermore,core and image logs indicated that the secondary porosity varies from 0%to 10%.The permeability indicator indicates that zones 3 and 5 have higher permeability index.Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility,mud loss and production index which vary between 1 and 1000 md.In addition,no relationship was observed between core porosity and permeability,while the permeability relied heavily on fracture aperture.Therefore,fracture aperture was considered as the most important parameter for the determination of permeability.Sudden changes were also observed at zones 1-1 and 5 in the permeability trend,due to the high fracture aperture.It can be concluded that the electrical image logs(FMI and EMI)are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin,SW Iran.展开更多
Image logs have higher resolution than conventional logs and it makes them powerful tools for detailed reservoir characterization and modeling. Hence, thin beds, which are not detectable using conventional logs, can b...Image logs have higher resolution than conventional logs and it makes them powerful tools for detailed reservoir characterization and modeling. Hence, thin beds, which are not detectable using conventional logs, can be characterized by image log data. The present study focuses on the Permian-Triassic carbonate reservoirs of Dalan and Kangan formations in one of the gas fields in southern Iran. In this study, the reservoir porosity was estimated using Fullbore Formation Microimager (FMI) image log. Then, the results were compared and validated with the core data. Porosity was estimated as 2-dimentional image by rescaling the FMI image log with Micro-Spherical Focused Log (MSFL). The image log-derived porosity was then compared with conventional logs-derived and core porosities. The image log-derived porosity is in good agreement with the core data. In this paper, a procedure was introduced which can be used to recognize very narrow pay layers (thickness in centimeters). This work is very important for identifying the proper perforation intervals in heterogeneous reservoirs.展开更多
Borehole acoustic reflection logging can provide high resolution images of nearborehole geological structure. However, the conventional seismic migration and imaging methods are not effective because the reflected wav...Borehole acoustic reflection logging can provide high resolution images of nearborehole geological structure. However, the conventional seismic migration and imaging methods are not effective because the reflected waves are interfered with the dominant borehole-guided modes and there are only eight receiving channels per shot available for stacking. In this paper, we apply an equivalent offset migration method based on wave scattering theory to process the acoustic reflection imaging log data from both numerical modeling and recorded field data. The result shows that, compared with the routine post-stack depth migration method, the equivalent offset migration method results in higher stack fold and is more effective for near-borehole structural imaging with low SNR acoustic reflection log data.展开更多
The laser scanning and CCD image-transmitting measurement method and principle on acquiring 3-D curved surface shape data are discussed. Computer processing technique of 3-D curved surface shape(be called“ 3 - D surf...The laser scanning and CCD image-transmitting measurement method and principle on acquiring 3-D curved surface shape data are discussed. Computer processing technique of 3-D curved surface shape(be called“ 3 - D surface shape”for short) data is analysed. This technique in- cludes these concrete methods and principles such as data smoothing, fitting, reconstructing ,elimi- nating and so on. The example and result about computer processing of 3- D surface shape data are given .展开更多
基金Supported by projects of the National Key Research and Development Plan(Nos.2017YFC0602203,2017YFC0601606)the National Science and Technology Major Project Task(No.2016ZX05027-002-003)+1 种基金the National Natural Science Foundation of China(Nos.41604089,41404089)the State Key Program of National Natural Science of China(No.41430322)
文摘The conventional methods of edge detection can roughly delineate edge position of geological bodies,but there are still some problems such as low detection accuracy and being susceptible to noise interference.In this paper,three image processing methods,Canny,Lo G and Sobel operators are briefly introduced,and applied to edge detection to determine the edge of geological bodies.Furthermore,model data is built to analyze the edge detection ability of this image processing methods,and compare with conventional methods.Combined with gravity anomaly of Sichuan basin and magnetic anomaly of Zhurihe area,the detection effect of image processing methods is further verified in real data.The results show that image processing methods can be applied to effectively identify the edge of geological bodies.Moreover,when both positive and negative anomalies exist and noise is abundant,fake edge can be avoided and edge division is clearer,and satisfactory results of edge detection are obtained.
基金the National Natural Science Foundation of China(42472194,42302153,and 42002144)the Fundamental Research Funds for the Central Univer-sities(22CX06002A).
文摘Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.
基金financial and data support from NISOC Oil Company.
文摘Assessment of reservoir and fracture parameters is necessary to optimize oil production,especially in heterogeneous reservoirs.Core and image logs are regarded as two of the best methods for this aim.However,due to core limitations,using image log is considered as the best method.This study aims to use electrical image logs in the carbonate Asmari Formation reservoir in Zagros Basin,SW Iran,in order to evaluate natural fractures,porosity system,permeability profile and heterogeneity index and accordingly compare the results with core and well data.The results indicated that the electrical image logs are reliable for evaluating fracture and reservoir parameters,when there is no core available for a well.Based on the results from formation micro-imager(FMI)and electrical micro-imager(EMI),Asmari was recognized as a completely fractured reservoir in studied field and the reservoir parameters are mainly controlled by fractures.Furthermore,core and image logs indicated that the secondary porosity varies from 0%to 10%.The permeability indicator indicates that zones 3 and 5 have higher permeability index.Image log permeability index shows a very reasonable permeability profile after scaling against core and modular dynamics tester mobility,mud loss and production index which vary between 1 and 1000 md.In addition,no relationship was observed between core porosity and permeability,while the permeability relied heavily on fracture aperture.Therefore,fracture aperture was considered as the most important parameter for the determination of permeability.Sudden changes were also observed at zones 1-1 and 5 in the permeability trend,due to the high fracture aperture.It can be concluded that the electrical image logs(FMI and EMI)are usable for evaluating both reservoir and fracture parameters in wells with no core data in the Zagros Basin,SW Iran.
文摘Image logs have higher resolution than conventional logs and it makes them powerful tools for detailed reservoir characterization and modeling. Hence, thin beds, which are not detectable using conventional logs, can be characterized by image log data. The present study focuses on the Permian-Triassic carbonate reservoirs of Dalan and Kangan formations in one of the gas fields in southern Iran. In this study, the reservoir porosity was estimated using Fullbore Formation Microimager (FMI) image log. Then, the results were compared and validated with the core data. Porosity was estimated as 2-dimentional image by rescaling the FMI image log with Micro-Spherical Focused Log (MSFL). The image log-derived porosity was then compared with conventional logs-derived and core porosities. The image log-derived porosity is in good agreement with the core data. In this paper, a procedure was introduced which can be used to recognize very narrow pay layers (thickness in centimeters). This work is very important for identifying the proper perforation intervals in heterogeneous reservoirs.
基金supported by the National Natural Science Foundation of China (Grant No.50674098)the 863 Program (Grant No.2006AA06Z207 & 2006AA06Z213)the 973 Program (Grant No.2007CB209601)
文摘Borehole acoustic reflection logging can provide high resolution images of nearborehole geological structure. However, the conventional seismic migration and imaging methods are not effective because the reflected waves are interfered with the dominant borehole-guided modes and there are only eight receiving channels per shot available for stacking. In this paper, we apply an equivalent offset migration method based on wave scattering theory to process the acoustic reflection imaging log data from both numerical modeling and recorded field data. The result shows that, compared with the routine post-stack depth migration method, the equivalent offset migration method results in higher stack fold and is more effective for near-borehole structural imaging with low SNR acoustic reflection log data.
文摘The laser scanning and CCD image-transmitting measurement method and principle on acquiring 3-D curved surface shape data are discussed. Computer processing technique of 3-D curved surface shape(be called“ 3 - D surface shape”for short) data is analysed. This technique in- cludes these concrete methods and principles such as data smoothing, fitting, reconstructing ,elimi- nating and so on. The example and result about computer processing of 3- D surface shape data are given .
