The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Fei Tian - One of the best experts on this subject based on the ideXlab platform.
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electrofacies classification of deeply buried carbonate strata using machine learning methods a case study on ordovician Paleokarst reservoirs in tarim basin
Marine and Petroleum Geology, 2021Co-Authors: Wenhao Zheng, Fei Tian, Fuqi Cheng, Wei Xin, Xiaocai ShanAbstract:Abstract The Paleokarst system is one of the main carbonate reservoirs, which can form important super-large oil fields. There are many typical Paleokarst reservoirs in the Tarim Basin Ordovician strata, mainly composed of caves, vugs, and fractures. Due to the deep burial depth and strong heterogeneity, qualitative identifying the different scale fracture-vuggy reservoirs from the tight limestone around the wellbore is a real challenge in the industrial community. In this paper, machine learning methods were used to classify electrofacies. Firstly, core samples and electrical imaging logging of the Paleokarst reservoirs are observed in detail and a core-electrical imaging chart was established. Secondly, conventional logging data was optimized and preprocessed for data mining, using Principal Component Analysis (PCA) algorithm and K-means algorithm. High-resolution electrical imaging logging was chosen as a constraint to recognize electrofacies, and an electrofacies-lithology database was established. Thirdly, based on the electrofacies-lithology database, Linear Discriminant Analysis (LDA) algorithm was used to build an electrofacies prediction model, which can automatically identify the electrofacies in carbonate strata, with a coincidence rate of 92.2%. Finally, the model was used to quantitatively recognize Paleokarst reservoirs and their distributions. The electrofacies machine learning workflow proposed in this paper could be used in Tarim Basin and other similar Paleokarst reservoirs, which can improve exploration efficiency and save economic cost.
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application of geologically constrained machine learning method in characterizing Paleokarst reservoirs of tarim basin china
Water, 2020Co-Authors: Wei Xin, Fei Tian, Xiaocai Shan, Yongjian Zhou, Huazhong Rong, Changchun YangAbstract:As deep carbonate fracture-cavity Paleokarst reservoirs are deeply buried and highly heterogeneous, and the responded seismic signals have weak amplitudes and low signal-to-noise ratios. Machine learning in seismic exploration provides a new perspective to solve the above problems, which is rapidly developing with compelling results. Applying machine learning algorithms directly on deep seismic signals or seismic attributes of deep carbonate fracture-cavity reservoirs without any prior knowledge constraints will result in wasted computation and reduce the accuracy. We propose a method of combining geological constraints and machine learning to describe deep carbonate fracture-cavity Paleokarst reservoirs. By empirical mode decomposition, the time–frequency features of the seismic data are obtained and then a sensitive frequency is selected using geological prior constraints, which is input to fuzzy C-means cluster for characterizing the reservoir distribution. Application on Tahe oilfield data shows the potential of highlighting subtle geologic structures that might otherwise escape unnoticed by applying machine learning directly.
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hydrothermal dolomite Paleokarst reservoir development in wolonghe gasfield sichuan basin revealed by seismic characterization
Water, 2020Co-Authors: Bole Gao, Fei Tian, Renfang Pan, Wenhao Zheng, Tianjun Huang, Yisheng LiuAbstract:Hydrothermal dolomite Paleokarst reservoir is a type of porous carbonate reservoir, which has a secondary porosity and can store a large amount of oil and gas underground. The reservoir is formed by magnesium-rich hydrothermal fluids during the karstification and later stages of the transformation. Due to the strong heterogeneity and thin thickness of hydrothermal dolomite Paleokarst reservoirs, it is a real challenge to characterize the spatial distribution of the reservoirs. In this paper, we studied the hydrothermal dolomite Paleokarst reservoir in the Wolonghe gasfield of the eastern Sichuan Basin. First, based on detailed observations of core samples, the characteristics and storage space types of the dolomite reservoir were described. Secondly, the petrophysical parameters of the Paleokarst reservoirs were analyzed, and then the indicator factor for the dolomite reservoirs was established. Thirdly, using the time–depth conversion method, the geological characteristics near boreholes were connected with a three-dimensional (3D) seismic dataset. Several petrophysical parameters were predicted by prestack synchronous inversion technology, including the P-wave velocity, S-wave velocity, P-wave impedance, and the hydrothermal dolomite Paleokarst reservoir indicator factor. Finally, the hydrothermal dolomite Paleokarst reservoirs were quantitatively predicted, and their distribution model was built. The 3D geophysical characterization approach improves our understanding of hydrothermal dolomite Paleokarst reservoirs, and can also be applied to other similar heterogeneous reservoirs.
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three dimensional geophysical characterization of deeply buried Paleokarst system in the tahe oilfield tarim basin china
Water, 2019Co-Authors: Fei Tian, Zhongxing Wang, Fuqi Cheng, Wei Xin, Olalekan Fayemi, Wang Zhang, Xiaocai ShanAbstract:Paleokarst reservoirs are the major type of the Ordovician carbonate reservoirs in the Tahe Oilfield. Due to the strong heterogeneity in distribution, it is a real challenge to detect the spatial distribution of Paleokarst reservoirs, especially those deeply buried more than 5500 m in the Tahe area. Based on the abundant core samples, this paper first described the structure of paleocaves drilled by well. Second, after time–depth conversions, the results from drilled wells were tied to three-dimensional (3D) seismic datasets, and then the threshold of host rocks and caves in wave impedance were identified. Third, the seismic-scale mapping and visualization of the Paleokarst reservoirs were achieved by tracing the distribution of paleocaves. This approach was applied in the well T403 area, and the structure of the Paleokarst, especially the runoff zone, was interpreted. 3D structure and spatial distribution of the Paleokarst system was demonstrated by plane, vertical, and 3D models. Additionally, according to the hydrology genetic relationships, the paleocaves in the runoff zone were divided into sinkholes, main channel, and branch channel. The approach of a 3D geophysical characterization of a deeply buried Paleokarst system can be applicable to Tahe and other similar Paleokarst oilfields, which will guide hydrocarbon exploration in Paleokarst reservoirs.
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multiscale geological geophysical characterization of the epigenic origin and deeply buried Paleokarst system in tahe oilfield tarim basin
Marine and Petroleum Geology, 2019Co-Authors: Fei Tian, Fuqi Cheng, Wang Zhang, Qiang Jin, Lamei Lin, Yan Wang, Debin Yang, Congkai NiuAbstract:Abstract The irregular structures of Paleokarst systems are among the major factors resulting in carbonate reservoir heterogeneity, which restricts the further exploration of Paleokarst reservoirs. Using multiscale geological-geophysical (G&G) data, including thin-section, core-sample and well-log data, the caves, cave fillings and fractures in individual wells in the northern Tahe Oilfield were characterized. Guided by seismic datasets, the interwell Paleokarst structures were interpreted in profile and plane view. The Paleokarst system in the study area was divided into four vertical zones, and the relative genetic structures were classified in the upper three zones. The epikarst zone includes paleo-canyons, karst towers, dolines, paleo-soils, and fractures, and the water flows horizontally. The vadose zone includes vadose caves and fractures, and the water flows vertically. The epiphreatic zone includes chambers, main channels, branch channels and fractures, and the water flows primarily horizontally along the water table. For each genetic type, the total hydrocarbon production and monthly production were analyzed. Furthermore, a genetic classification model was built for the Paleokarst reservoirs of the study area, which increased our understanding of the structure of the Paleokarst system. The results of the structure classification and production analysis can effectively guide hydrocarbon exploration, and this approach can be applied to similar Paleokarst reservoirs.
Xiaocai Shan - One of the best experts on this subject based on the ideXlab platform.
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electrofacies classification of deeply buried carbonate strata using machine learning methods a case study on ordovician Paleokarst reservoirs in tarim basin
Marine and Petroleum Geology, 2021Co-Authors: Wenhao Zheng, Fei Tian, Fuqi Cheng, Wei Xin, Xiaocai ShanAbstract:Abstract The Paleokarst system is one of the main carbonate reservoirs, which can form important super-large oil fields. There are many typical Paleokarst reservoirs in the Tarim Basin Ordovician strata, mainly composed of caves, vugs, and fractures. Due to the deep burial depth and strong heterogeneity, qualitative identifying the different scale fracture-vuggy reservoirs from the tight limestone around the wellbore is a real challenge in the industrial community. In this paper, machine learning methods were used to classify electrofacies. Firstly, core samples and electrical imaging logging of the Paleokarst reservoirs are observed in detail and a core-electrical imaging chart was established. Secondly, conventional logging data was optimized and preprocessed for data mining, using Principal Component Analysis (PCA) algorithm and K-means algorithm. High-resolution electrical imaging logging was chosen as a constraint to recognize electrofacies, and an electrofacies-lithology database was established. Thirdly, based on the electrofacies-lithology database, Linear Discriminant Analysis (LDA) algorithm was used to build an electrofacies prediction model, which can automatically identify the electrofacies in carbonate strata, with a coincidence rate of 92.2%. Finally, the model was used to quantitatively recognize Paleokarst reservoirs and their distributions. The electrofacies machine learning workflow proposed in this paper could be used in Tarim Basin and other similar Paleokarst reservoirs, which can improve exploration efficiency and save economic cost.
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application of geologically constrained machine learning method in characterizing Paleokarst reservoirs of tarim basin china
Water, 2020Co-Authors: Wei Xin, Fei Tian, Xiaocai Shan, Yongjian Zhou, Huazhong Rong, Changchun YangAbstract:As deep carbonate fracture-cavity Paleokarst reservoirs are deeply buried and highly heterogeneous, and the responded seismic signals have weak amplitudes and low signal-to-noise ratios. Machine learning in seismic exploration provides a new perspective to solve the above problems, which is rapidly developing with compelling results. Applying machine learning algorithms directly on deep seismic signals or seismic attributes of deep carbonate fracture-cavity reservoirs without any prior knowledge constraints will result in wasted computation and reduce the accuracy. We propose a method of combining geological constraints and machine learning to describe deep carbonate fracture-cavity Paleokarst reservoirs. By empirical mode decomposition, the time–frequency features of the seismic data are obtained and then a sensitive frequency is selected using geological prior constraints, which is input to fuzzy C-means cluster for characterizing the reservoir distribution. Application on Tahe oilfield data shows the potential of highlighting subtle geologic structures that might otherwise escape unnoticed by applying machine learning directly.
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three dimensional geophysical characterization of deeply buried Paleokarst system in the tahe oilfield tarim basin china
Water, 2019Co-Authors: Fei Tian, Zhongxing Wang, Fuqi Cheng, Wei Xin, Olalekan Fayemi, Wang Zhang, Xiaocai ShanAbstract:Paleokarst reservoirs are the major type of the Ordovician carbonate reservoirs in the Tahe Oilfield. Due to the strong heterogeneity in distribution, it is a real challenge to detect the spatial distribution of Paleokarst reservoirs, especially those deeply buried more than 5500 m in the Tahe area. Based on the abundant core samples, this paper first described the structure of paleocaves drilled by well. Second, after time–depth conversions, the results from drilled wells were tied to three-dimensional (3D) seismic datasets, and then the threshold of host rocks and caves in wave impedance were identified. Third, the seismic-scale mapping and visualization of the Paleokarst reservoirs were achieved by tracing the distribution of paleocaves. This approach was applied in the well T403 area, and the structure of the Paleokarst, especially the runoff zone, was interpreted. 3D structure and spatial distribution of the Paleokarst system was demonstrated by plane, vertical, and 3D models. Additionally, according to the hydrology genetic relationships, the paleocaves in the runoff zone were divided into sinkholes, main channel, and branch channel. The approach of a 3D geophysical characterization of a deeply buried Paleokarst system can be applicable to Tahe and other similar Paleokarst oilfields, which will guide hydrocarbon exploration in Paleokarst reservoirs.
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spectral decomposition and a waveform cluster to characterize strongly heterogeneous Paleokarst reservoirs in the tarim basin china
Water, 2019Co-Authors: Xiaocai Shan, Fei Tian, Fuqi Cheng, Changchun Yang, Wei XinAbstract:The main components of the Ordovician carbonate reservoirs in the Tahe Oilfield are Paleokarst fracture-cavity paleo-channel systems formed by karstification. Detailed characterization of these Paleokarst reservoirs is challenging because of heterogeneities in characteristics and strong vertical and lateral non-uniformities. Traditional seismic analysis methods are not able to solve the identification problem of such strongly heterogeneous reservoirs. Recent developments in seismic interpretation have heightened the need to describe the fracture-cavity structure of a paleo-channel with more accuracy. We propose a new prediction model for fracture-cavity carbonate reservoirs based on spectral decomposition and a waveform cluster. By the Matching Pursuit decomposition algorithm, the single-frequency data volumes are obtained. The specific frequency data volume that is the most sensitive to the reservoir is chosen based on seismic synthesis traces of well-logging data and geological interpretability. The waveform cluster is then applied to delineate the complex Paleokarst systems, particularly the fracture-caves in the runoff zone. This method was applied to the area around Well T615 in the Tahe oilfield, and a Paleokarst fracture-cavity system with strong heterogeneity in the runoff zone was delineated and characterized. The findings of this research provide insights for predicting other similar karst systems, such as karstic groundwater and karst hydrogeological systems.
Wei Xin - One of the best experts on this subject based on the ideXlab platform.
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electrofacies classification of deeply buried carbonate strata using machine learning methods a case study on ordovician Paleokarst reservoirs in tarim basin
Marine and Petroleum Geology, 2021Co-Authors: Wenhao Zheng, Fei Tian, Fuqi Cheng, Wei Xin, Xiaocai ShanAbstract:Abstract The Paleokarst system is one of the main carbonate reservoirs, which can form important super-large oil fields. There are many typical Paleokarst reservoirs in the Tarim Basin Ordovician strata, mainly composed of caves, vugs, and fractures. Due to the deep burial depth and strong heterogeneity, qualitative identifying the different scale fracture-vuggy reservoirs from the tight limestone around the wellbore is a real challenge in the industrial community. In this paper, machine learning methods were used to classify electrofacies. Firstly, core samples and electrical imaging logging of the Paleokarst reservoirs are observed in detail and a core-electrical imaging chart was established. Secondly, conventional logging data was optimized and preprocessed for data mining, using Principal Component Analysis (PCA) algorithm and K-means algorithm. High-resolution electrical imaging logging was chosen as a constraint to recognize electrofacies, and an electrofacies-lithology database was established. Thirdly, based on the electrofacies-lithology database, Linear Discriminant Analysis (LDA) algorithm was used to build an electrofacies prediction model, which can automatically identify the electrofacies in carbonate strata, with a coincidence rate of 92.2%. Finally, the model was used to quantitatively recognize Paleokarst reservoirs and their distributions. The electrofacies machine learning workflow proposed in this paper could be used in Tarim Basin and other similar Paleokarst reservoirs, which can improve exploration efficiency and save economic cost.
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application of geologically constrained machine learning method in characterizing Paleokarst reservoirs of tarim basin china
Water, 2020Co-Authors: Wei Xin, Fei Tian, Xiaocai Shan, Yongjian Zhou, Huazhong Rong, Changchun YangAbstract:As deep carbonate fracture-cavity Paleokarst reservoirs are deeply buried and highly heterogeneous, and the responded seismic signals have weak amplitudes and low signal-to-noise ratios. Machine learning in seismic exploration provides a new perspective to solve the above problems, which is rapidly developing with compelling results. Applying machine learning algorithms directly on deep seismic signals or seismic attributes of deep carbonate fracture-cavity reservoirs without any prior knowledge constraints will result in wasted computation and reduce the accuracy. We propose a method of combining geological constraints and machine learning to describe deep carbonate fracture-cavity Paleokarst reservoirs. By empirical mode decomposition, the time–frequency features of the seismic data are obtained and then a sensitive frequency is selected using geological prior constraints, which is input to fuzzy C-means cluster for characterizing the reservoir distribution. Application on Tahe oilfield data shows the potential of highlighting subtle geologic structures that might otherwise escape unnoticed by applying machine learning directly.
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three dimensional geophysical characterization of deeply buried Paleokarst system in the tahe oilfield tarim basin china
Water, 2019Co-Authors: Fei Tian, Zhongxing Wang, Fuqi Cheng, Wei Xin, Olalekan Fayemi, Wang Zhang, Xiaocai ShanAbstract:Paleokarst reservoirs are the major type of the Ordovician carbonate reservoirs in the Tahe Oilfield. Due to the strong heterogeneity in distribution, it is a real challenge to detect the spatial distribution of Paleokarst reservoirs, especially those deeply buried more than 5500 m in the Tahe area. Based on the abundant core samples, this paper first described the structure of paleocaves drilled by well. Second, after time–depth conversions, the results from drilled wells were tied to three-dimensional (3D) seismic datasets, and then the threshold of host rocks and caves in wave impedance were identified. Third, the seismic-scale mapping and visualization of the Paleokarst reservoirs were achieved by tracing the distribution of paleocaves. This approach was applied in the well T403 area, and the structure of the Paleokarst, especially the runoff zone, was interpreted. 3D structure and spatial distribution of the Paleokarst system was demonstrated by plane, vertical, and 3D models. Additionally, according to the hydrology genetic relationships, the paleocaves in the runoff zone were divided into sinkholes, main channel, and branch channel. The approach of a 3D geophysical characterization of a deeply buried Paleokarst system can be applicable to Tahe and other similar Paleokarst oilfields, which will guide hydrocarbon exploration in Paleokarst reservoirs.
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spectral decomposition and a waveform cluster to characterize strongly heterogeneous Paleokarst reservoirs in the tarim basin china
Water, 2019Co-Authors: Xiaocai Shan, Fei Tian, Fuqi Cheng, Changchun Yang, Wei XinAbstract:The main components of the Ordovician carbonate reservoirs in the Tahe Oilfield are Paleokarst fracture-cavity paleo-channel systems formed by karstification. Detailed characterization of these Paleokarst reservoirs is challenging because of heterogeneities in characteristics and strong vertical and lateral non-uniformities. Traditional seismic analysis methods are not able to solve the identification problem of such strongly heterogeneous reservoirs. Recent developments in seismic interpretation have heightened the need to describe the fracture-cavity structure of a paleo-channel with more accuracy. We propose a new prediction model for fracture-cavity carbonate reservoirs based on spectral decomposition and a waveform cluster. By the Matching Pursuit decomposition algorithm, the single-frequency data volumes are obtained. The specific frequency data volume that is the most sensitive to the reservoir is chosen based on seismic synthesis traces of well-logging data and geological interpretability. The waveform cluster is then applied to delineate the complex Paleokarst systems, particularly the fracture-caves in the runoff zone. This method was applied to the area around Well T615 in the Tahe oilfield, and a Paleokarst fracture-cavity system with strong heterogeneity in the runoff zone was delineated and characterized. The findings of this research provide insights for predicting other similar karst systems, such as karstic groundwater and karst hydrogeological systems.
Fuqi Cheng - One of the best experts on this subject based on the ideXlab platform.
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electrofacies classification of deeply buried carbonate strata using machine learning methods a case study on ordovician Paleokarst reservoirs in tarim basin
Marine and Petroleum Geology, 2021Co-Authors: Wenhao Zheng, Fei Tian, Fuqi Cheng, Wei Xin, Xiaocai ShanAbstract:Abstract The Paleokarst system is one of the main carbonate reservoirs, which can form important super-large oil fields. There are many typical Paleokarst reservoirs in the Tarim Basin Ordovician strata, mainly composed of caves, vugs, and fractures. Due to the deep burial depth and strong heterogeneity, qualitative identifying the different scale fracture-vuggy reservoirs from the tight limestone around the wellbore is a real challenge in the industrial community. In this paper, machine learning methods were used to classify electrofacies. Firstly, core samples and electrical imaging logging of the Paleokarst reservoirs are observed in detail and a core-electrical imaging chart was established. Secondly, conventional logging data was optimized and preprocessed for data mining, using Principal Component Analysis (PCA) algorithm and K-means algorithm. High-resolution electrical imaging logging was chosen as a constraint to recognize electrofacies, and an electrofacies-lithology database was established. Thirdly, based on the electrofacies-lithology database, Linear Discriminant Analysis (LDA) algorithm was used to build an electrofacies prediction model, which can automatically identify the electrofacies in carbonate strata, with a coincidence rate of 92.2%. Finally, the model was used to quantitatively recognize Paleokarst reservoirs and their distributions. The electrofacies machine learning workflow proposed in this paper could be used in Tarim Basin and other similar Paleokarst reservoirs, which can improve exploration efficiency and save economic cost.
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three dimensional geophysical characterization of deeply buried Paleokarst system in the tahe oilfield tarim basin china
Water, 2019Co-Authors: Fei Tian, Zhongxing Wang, Fuqi Cheng, Wei Xin, Olalekan Fayemi, Wang Zhang, Xiaocai ShanAbstract:Paleokarst reservoirs are the major type of the Ordovician carbonate reservoirs in the Tahe Oilfield. Due to the strong heterogeneity in distribution, it is a real challenge to detect the spatial distribution of Paleokarst reservoirs, especially those deeply buried more than 5500 m in the Tahe area. Based on the abundant core samples, this paper first described the structure of paleocaves drilled by well. Second, after time–depth conversions, the results from drilled wells were tied to three-dimensional (3D) seismic datasets, and then the threshold of host rocks and caves in wave impedance were identified. Third, the seismic-scale mapping and visualization of the Paleokarst reservoirs were achieved by tracing the distribution of paleocaves. This approach was applied in the well T403 area, and the structure of the Paleokarst, especially the runoff zone, was interpreted. 3D structure and spatial distribution of the Paleokarst system was demonstrated by plane, vertical, and 3D models. Additionally, according to the hydrology genetic relationships, the paleocaves in the runoff zone were divided into sinkholes, main channel, and branch channel. The approach of a 3D geophysical characterization of a deeply buried Paleokarst system can be applicable to Tahe and other similar Paleokarst oilfields, which will guide hydrocarbon exploration in Paleokarst reservoirs.
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multiscale geological geophysical characterization of the epigenic origin and deeply buried Paleokarst system in tahe oilfield tarim basin
Marine and Petroleum Geology, 2019Co-Authors: Fei Tian, Fuqi Cheng, Wang Zhang, Qiang Jin, Lamei Lin, Yan Wang, Debin Yang, Congkai NiuAbstract:Abstract The irregular structures of Paleokarst systems are among the major factors resulting in carbonate reservoir heterogeneity, which restricts the further exploration of Paleokarst reservoirs. Using multiscale geological-geophysical (G&G) data, including thin-section, core-sample and well-log data, the caves, cave fillings and fractures in individual wells in the northern Tahe Oilfield were characterized. Guided by seismic datasets, the interwell Paleokarst structures were interpreted in profile and plane view. The Paleokarst system in the study area was divided into four vertical zones, and the relative genetic structures were classified in the upper three zones. The epikarst zone includes paleo-canyons, karst towers, dolines, paleo-soils, and fractures, and the water flows horizontally. The vadose zone includes vadose caves and fractures, and the water flows vertically. The epiphreatic zone includes chambers, main channels, branch channels and fractures, and the water flows primarily horizontally along the water table. For each genetic type, the total hydrocarbon production and monthly production were analyzed. Furthermore, a genetic classification model was built for the Paleokarst reservoirs of the study area, which increased our understanding of the structure of the Paleokarst system. The results of the structure classification and production analysis can effectively guide hydrocarbon exploration, and this approach can be applied to similar Paleokarst reservoirs.
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spectral decomposition and a waveform cluster to characterize strongly heterogeneous Paleokarst reservoirs in the tarim basin china
Water, 2019Co-Authors: Xiaocai Shan, Fei Tian, Fuqi Cheng, Changchun Yang, Wei XinAbstract:The main components of the Ordovician carbonate reservoirs in the Tahe Oilfield are Paleokarst fracture-cavity paleo-channel systems formed by karstification. Detailed characterization of these Paleokarst reservoirs is challenging because of heterogeneities in characteristics and strong vertical and lateral non-uniformities. Traditional seismic analysis methods are not able to solve the identification problem of such strongly heterogeneous reservoirs. Recent developments in seismic interpretation have heightened the need to describe the fracture-cavity structure of a paleo-channel with more accuracy. We propose a new prediction model for fracture-cavity carbonate reservoirs based on spectral decomposition and a waveform cluster. By the Matching Pursuit decomposition algorithm, the single-frequency data volumes are obtained. The specific frequency data volume that is the most sensitive to the reservoir is chosen based on seismic synthesis traces of well-logging data and geological interpretability. The waveform cluster is then applied to delineate the complex Paleokarst systems, particularly the fracture-caves in the runoff zone. This method was applied to the area around Well T615 in the Tahe oilfield, and a Paleokarst fracture-cavity system with strong heterogeneity in the runoff zone was delineated and characterized. The findings of this research provide insights for predicting other similar karst systems, such as karstic groundwater and karst hydrogeological systems.
Yuting Zhong - One of the best experts on this subject based on the ideXlab platform.
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reply to comment on Paleokarst on the top of the maokou formation further evidence for domal crustal uplift prior to the emeishan flood volcanism by bin he yi gang xu jun peng guan yu ting zhong lithos 119 1 9 2010
Lithos, 2011Co-Authors: Junpeng Guan, Yuting ZhongAbstract:We welcome the comments of Ukstins Peate et al. (2011) on our recent paper (He et al., 2010a), which provide us with an opportunity to extend our discussion on Paleokarst between theMaokou Formation and theEmeishanbasalts. Ukstins Peate et al. (2011)question the existence of karstic surface of the Maokou limestone described by He et al. (2010a). Instead theycontend that the earliest Emeishanmagmaswereeruptedat, or below, sea level in the center of the Emeishan Large Igneous Province (ELIP), rejecting the pre-volcanic domal uplift model proposed by He et al. (2003). Their arguments are largely basedon their interpretations of field observations such as (a) sedimentary continuity across the Guadalupian-Lopingian Boundary (G-LB) in the center of the ELIP and (b) the faulted contact between Emeishan basalt andMaokou Formation. However, aswewill showbelow,wedispute thesefieldobservations and Ukstins Peate et al.'s interpretation of the temporal and spatial frameworks of our, and others', results. In this reply, we will clarify, using specific examples, some key aspects of their questions about the Paleokarst on the Maokou Formation and its relationship to plumeinduced uplift in the ELIP.
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Paleokarst on the top of the maokou formation further evidence for domal crustal uplift prior to the emeishan flood volcanism
Lithos, 2010Co-Authors: Junpeng Guan, Yuting ZhongAbstract:Abstract The ~ 260 Ma Emeishan Large Igneous Province (ELIP) in southwest China has previously been demonstrated to provide compelling evidence for pre-volcanic crustal doming in support of the mantle plume hypothesis. However this has been questioned by Ukstins-Peate and Bryan (2008) by showing hydrothermal magmatic activity at the Daqiao section. To solve this argument, a detailed characterization of the contact between the Emeishan basalts and the Maokou Formation was carried out. The contact is shown to be an unconformity, which is characterized by Paleokarst on top of the Maokou Formation, including Paleokarst relief, sinkholes, caves, tower karst and its corresponding rocks (such as kaolinite, bauxite and ferruginous duricrust and collapsed breccias, etc.). This Paleokarst unconformity was in turn covered or infilled by the Emeishan basalts and tuffs, suggesting that uplift and erosion occurred prior to the eruption of the ELIP. The extent of erosion of the Maokou Formation indicates the ELIP can be divided into three roughly concentric zones: the inner, intermediate, and outer zones. The Paleokarst features on the top of Maokou Formation vary across the ELIP. In the inner zone, a likely sinkhole and an incision valley with 450 m relief in height are found. In the intermediate zone, various Paleokarst landforms such as karst relief, sinkholes and tower karsts are well developed. Some sinkholes that developed in the Qixia Formation below the Maokou Formation imply that the paleorelief is more than 350 m in height. In the outer zone, the Paleokarstic surface is a paleo-weathering layer with minor karstification and development of caves at 10–50 m. This spatial variation of the Paleokarst reflects variation of uplift height across the ELIP. The extent of minimal uplift is estimated to be at least 450 m in the inner zone, 350 m in the intermediate zone, whereas uplift is minor (tens-50 m) in the outer zone. The magnitude and shape of the uplift is roughly consistent with that predicted by mantle plume models. The Paleokarst was formed after the deposition of the Maokou Formation and the eruption of the Emeishan basalts at the end-Guadalupian and indicates a short duration of uplift. Thus this study lends further support to domal uplift prior to the Emeishan flood volcanism, but also to the mantle plume initiation model for the generation of the ELIP.