A key aspect in extracting quantitative information from FMI logs is to segment the FMI image to get images of pores, vugs and fractures. A segmentation method based on the dyadic wavelet transform in 2-D is introduce...A key aspect in extracting quantitative information from FMI logs is to segment the FMI image to get images of pores, vugs and fractures. A segmentation method based on the dyadic wavelet transform in 2-D is introduced in this paper. The first step is to find all the edge pixels of the FMI image using the 2-D wavelet transform. The second step is to calculate a segmentation threshold based on the average value of the edge pixels. Field data processing examples show that sub-images of vugs and fractures can be correctly separated from original FMI data continuously and automatically along the depth axis. The image segmentation lays the foundation for in-situ parameter calculation.展开更多
Natural open fractures(NOFs) in reservoir rocks are critical factors influencing permeability. Identifying these fractures and fractured zones typically involves analyzing core samples and image logs. However,core dat...Natural open fractures(NOFs) in reservoir rocks are critical factors influencing permeability. Identifying these fractures and fractured zones typically involves analyzing core samples and image logs. However,core data are limited topecific depths within the reservoir, and image log data are confined to a small number of wells. In this study, fracture facies in a carbonate reservoir(Kangan-Dalan Formation) were predicted using Formation Micro-Imager(FMI) logs, conventional well logs, and petrophysical parameters, with a machine learning algorithm. Initially, open fractures were identified in wells A and B using the FMI log. In well A, the open fractures exhibit an average dip of 61°, an azimuth of N79E, and a strike direction of N11W/S11E. In well B, the fractures have an average dip of 69°, an azimuth of N26E, and a strike direction of N64W/S64E. Subsequently, fracture density logs for wells A and B were calculated,with average values of 0.41 and 0.33, respectively. Conventional well logs, including density(RHOB),sonic(DT), and petrophysical parameters, specifically effective porosity(PHIE), were used as input data for a Multi-Resolution Graph-Based Clustering(MRGC) algorithm, which is one of the machine learning algorithms employed in this study. Additionally, a synthetic log called FLAG, derived from the fracture density log(with values of 0 and 1 indicating the presence or absence of fractures), was incorporated into the algorithm as an associated input log. This algorithm enabled the identification of fracture facies,representing open fractures or fractured zones, in well A. To evaluate the accuracy of the algorithm, the results obtained were compared with two other clustering algorithms: Ascendant Hierarchical Clustering(AHC) and Self-Organizing Maps(SOM). Well B was used as a blind test to validate the clustering model.In this test, the clustering algorithm was applied excluding the FLAG synthetic log derived from the FMI log. The results from well B demonstrated that the developed algorithm accurately identifies fracture facies in wells lacking image log and core data. The algorithm was subsequently extended to wells C and D, which lacked core or image log data. Fractured zones in these wells were successfully identified as fracture facies. Additionally, a two-dimensional map of fracture facies thickness was generated for the study area. The developed hybrid algorithm demonstrated strong potential for generalizing to other wells in the field, enabling fracture facies modeling in both 2D and 3D.展开更多
Image processing plays an important role in engineering treatment. The authors mainly introduced the feature recognition of borehole image process based on Ant Colony Algorithm (ACA). The most important geological str...Image processing plays an important role in engineering treatment. The authors mainly introduced the feature recognition of borehole image process based on Ant Colony Algorithm (ACA). The most important geological structure-fracture on the borehole image was identified, and quantitative parameters were obtained by HOUGH transform. Several case studies show that the method is feasible.展开更多
基金The research was supported by the FifteenthNational Scientific and Technological Project (2001-BA605A-03-02)
文摘A key aspect in extracting quantitative information from FMI logs is to segment the FMI image to get images of pores, vugs and fractures. A segmentation method based on the dyadic wavelet transform in 2-D is introduced in this paper. The first step is to find all the edge pixels of the FMI image using the 2-D wavelet transform. The second step is to calculate a segmentation threshold based on the average value of the edge pixels. Field data processing examples show that sub-images of vugs and fractures can be correctly separated from original FMI data continuously and automatically along the depth axis. The image segmentation lays the foundation for in-situ parameter calculation.
文摘Natural open fractures(NOFs) in reservoir rocks are critical factors influencing permeability. Identifying these fractures and fractured zones typically involves analyzing core samples and image logs. However,core data are limited topecific depths within the reservoir, and image log data are confined to a small number of wells. In this study, fracture facies in a carbonate reservoir(Kangan-Dalan Formation) were predicted using Formation Micro-Imager(FMI) logs, conventional well logs, and petrophysical parameters, with a machine learning algorithm. Initially, open fractures were identified in wells A and B using the FMI log. In well A, the open fractures exhibit an average dip of 61°, an azimuth of N79E, and a strike direction of N11W/S11E. In well B, the fractures have an average dip of 69°, an azimuth of N26E, and a strike direction of N64W/S64E. Subsequently, fracture density logs for wells A and B were calculated,with average values of 0.41 and 0.33, respectively. Conventional well logs, including density(RHOB),sonic(DT), and petrophysical parameters, specifically effective porosity(PHIE), were used as input data for a Multi-Resolution Graph-Based Clustering(MRGC) algorithm, which is one of the machine learning algorithms employed in this study. Additionally, a synthetic log called FLAG, derived from the fracture density log(with values of 0 and 1 indicating the presence or absence of fractures), was incorporated into the algorithm as an associated input log. This algorithm enabled the identification of fracture facies,representing open fractures or fractured zones, in well A. To evaluate the accuracy of the algorithm, the results obtained were compared with two other clustering algorithms: Ascendant Hierarchical Clustering(AHC) and Self-Organizing Maps(SOM). Well B was used as a blind test to validate the clustering model.In this test, the clustering algorithm was applied excluding the FLAG synthetic log derived from the FMI log. The results from well B demonstrated that the developed algorithm accurately identifies fracture facies in wells lacking image log and core data. The algorithm was subsequently extended to wells C and D, which lacked core or image log data. Fractured zones in these wells were successfully identified as fracture facies. Additionally, a two-dimensional map of fracture facies thickness was generated for the study area. The developed hybrid algorithm demonstrated strong potential for generalizing to other wells in the field, enabling fracture facies modeling in both 2D and 3D.
文摘Image processing plays an important role in engineering treatment. The authors mainly introduced the feature recognition of borehole image process based on Ant Colony Algorithm (ACA). The most important geological structure-fracture on the borehole image was identified, and quantitative parameters were obtained by HOUGH transform. Several case studies show that the method is feasible.
