Enhancing vertical resolution and signal-to-noise ratio are key objectives in the seismic data processing. Considering the underground medium is inhomogeneous and incompletely elastic, seismic wave energy attenuation occurs during underground propagation, which has a significant impact on seismic data resolution and signal-to-noise ratio. Traditional fast-matching pursuit algorithms make it difficult to separate valid signals and noise effectively while reconstructing the noisy signals. Therefore, an improved fast-matching pursuit algorithm that combines the variational modal decomposition (VMD) strategy is developed. The VMD algorithm is used to obtain intrinsic mode functions with varying amplitudes, frequencies, and center times. It can achieve a multi-scale decomposition of non-stationary seismic data. Based on the intrinsic mode functions of different scales, the fast matching pursuit algorithm can reconstruct prior information of the amplitude, frequency, and center time of valid signals and noise signals in the mode functions. Thus, the high-resolution sparse representation of intrinsic mode functions is achieved. The numerical results indicate that the proposed method not only separates the effective signal and noise but also preserves the valid signal as much as possible. In addition, the feasibility of the method is further verified by field exploration data. The results show that this strategy can enhance the resolution of seismic data while restoring the attenuated energy using multi-scale seismic data.
{"title":"Seismic resolution enhancement with variational modal based fast matching pursuit decomposition","authors":"Chaohe Wang, Zhaoyun Zong, Xingyao Yin, Kun Li","doi":"10.1190/int-2023-0002.1","DOIUrl":"https://doi.org/10.1190/int-2023-0002.1","url":null,"abstract":"Enhancing vertical resolution and signal-to-noise ratio are key objectives in the seismic data processing. Considering the underground medium is inhomogeneous and incompletely elastic, seismic wave energy attenuation occurs during underground propagation, which has a significant impact on seismic data resolution and signal-to-noise ratio. Traditional fast-matching pursuit algorithms make it difficult to separate valid signals and noise effectively while reconstructing the noisy signals. Therefore, an improved fast-matching pursuit algorithm that combines the variational modal decomposition (VMD) strategy is developed. The VMD algorithm is used to obtain intrinsic mode functions with varying amplitudes, frequencies, and center times. It can achieve a multi-scale decomposition of non-stationary seismic data. Based on the intrinsic mode functions of different scales, the fast matching pursuit algorithm can reconstruct prior information of the amplitude, frequency, and center time of valid signals and noise signals in the mode functions. Thus, the high-resolution sparse representation of intrinsic mode functions is achieved. The numerical results indicate that the proposed method not only separates the effective signal and noise but also preserves the valid signal as much as possible. In addition, the feasibility of the method is further verified by field exploration data. The results show that this strategy can enhance the resolution of seismic data while restoring the attenuated energy using multi-scale seismic data.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135475469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The characteristics and formation of maximum flooding (MF) black shales are important aspects in defining the geology of fine-grained reservoirs. The MF black shales are located at the bottom of the Longmaxi Formation on the Upper Yangtze Platform, corresponding to graptolite zone LM1. Seismic interpretation, good correlation, X-ray diffraction whole- rock analysis, total organic carbon (TOC) tests, and field emission scanning electron microscopy analysis showed that the MF black shales have an average content of 49.3% quartz (85% clay size), 10.5% calcite, 8.4% dolomite, and 23.4% clay minerals. The quartz content increases basinward, whereas the clay mineral content decreases. The shale developed during rapid sea level rise, with a thickness of 0.52.8 m that gradually thickens basinward. The TOC content, averaging 5.4%, gradually decreases basinward, with four distinct stacking patterns. The mineral composition and thickness of the Longmaxi shale are related closely to rapid transgression, biology, and volcanism during the period of sedimentation. Rapid transgression has led to a decrease in terrestrial input and shale thickness. In addition, biological activity and volcanism have caused the prevalence of microcrystalline quartz. Shales with high TOC content are related to anoxic conditions, along with low sedimentation rates and high primary productivity. The combination of an anoxic water column, weak dilution, and enhanced organic matter supply enhanced the preservation of the organic matter. The four TOC stacking patterns are related to the water depth. The supply of clay minerals decreases with increasing water depth, whereas the degradation and recycling of organic matter decrease the TOC content. The sediment accommodation increases with increasing water depth, resulting in four TOC stacking patterns.
{"title":"The Lower Silurian Longmaxi rapid-transgressive black shale and organic matter distribution on the Upper Yangtze Platform, China","authors":"Zhensheng Shi, Tianqi Zhou, Ling Qi","doi":"10.1190/int-2023-0058.1","DOIUrl":"https://doi.org/10.1190/int-2023-0058.1","url":null,"abstract":"The characteristics and formation of maximum flooding (MF) black shales are important aspects in defining the geology of fine-grained reservoirs. The MF black shales are located at the bottom of the Longmaxi Formation on the Upper Yangtze Platform, corresponding to graptolite zone LM1. Seismic interpretation, good correlation, X-ray diffraction whole- rock analysis, total organic carbon (TOC) tests, and field emission scanning electron microscopy analysis showed that the MF black shales have an average content of 49.3% quartz (85% clay size), 10.5% calcite, 8.4% dolomite, and 23.4% clay minerals. The quartz content increases basinward, whereas the clay mineral content decreases. The shale developed during rapid sea level rise, with a thickness of 0.52.8 m that gradually thickens basinward. The TOC content, averaging 5.4%, gradually decreases basinward, with four distinct stacking patterns. The mineral composition and thickness of the Longmaxi shale are related closely to rapid transgression, biology, and volcanism during the period of sedimentation. Rapid transgression has led to a decrease in terrestrial input and shale thickness. In addition, biological activity and volcanism have caused the prevalence of microcrystalline quartz. Shales with high TOC content are related to anoxic conditions, along with low sedimentation rates and high primary productivity. The combination of an anoxic water column, weak dilution, and enhanced organic matter supply enhanced the preservation of the organic matter. The four TOC stacking patterns are related to the water depth. The supply of clay minerals decreases with increasing water depth, whereas the degradation and recycling of organic matter decrease the TOC content. The sediment accommodation increases with increasing water depth, resulting in four TOC stacking patterns.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135679378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Leebyn Chong, Timothy S. Collett, C. Gabriel Creason, Yongkoo Seol, Evgeniy M. Myshakin
Artificial Neural Networks (ANN) were used to assess methane hydrate occurrence and saturation in marine sediments offshore India. The ANN analysis classifies the gas hydrate occurrence into three types: methane hydrate in pore space, methane hydrate in fractures, or no methane hydrate. Further, predicted saturation characterizes the volume of gas hydrate with respect to the available void volume. Log data collected at six wells, which were drilled during the India National Gas Hydrate Program Expedition 02 (NGHP-02), provided a combination of well log measurements that were used as input for machine learning (ML) models. Well log measurements included density, porosity, electrical resistivity, natural gamma radiation, and acoustic wave velocity. Combinations of well logs used in the ML models provide good overall balanced accuracy (0.79 to 0.86) for the prediction of the gas hydrate occurrence and good accuracy (0.68 to 0.92) for methane hydrate saturation prediction in the marine accumulations against reference data. The accuracy scores indicate that the ML models can successfully predict reservoir characteristics for marine methane hydrate deposits. The results indicate that the ML models can either augment physics-driven methods for assessing the occurrence and saturation of methane hydrate deposits or serve as an independent predictive tool for those characteristics.
{"title":"Machine Learning Application to Assess Occurrence and Saturations of Methane Hydrate in Marine Deposits Offshore India","authors":"Leebyn Chong, Timothy S. Collett, C. Gabriel Creason, Yongkoo Seol, Evgeniy M. Myshakin","doi":"10.1190/int-2023-0056.1","DOIUrl":"https://doi.org/10.1190/int-2023-0056.1","url":null,"abstract":"Artificial Neural Networks (ANN) were used to assess methane hydrate occurrence and saturation in marine sediments offshore India. The ANN analysis classifies the gas hydrate occurrence into three types: methane hydrate in pore space, methane hydrate in fractures, or no methane hydrate. Further, predicted saturation characterizes the volume of gas hydrate with respect to the available void volume. Log data collected at six wells, which were drilled during the India National Gas Hydrate Program Expedition 02 (NGHP-02), provided a combination of well log measurements that were used as input for machine learning (ML) models. Well log measurements included density, porosity, electrical resistivity, natural gamma radiation, and acoustic wave velocity. Combinations of well logs used in the ML models provide good overall balanced accuracy (0.79 to 0.86) for the prediction of the gas hydrate occurrence and good accuracy (0.68 to 0.92) for methane hydrate saturation prediction in the marine accumulations against reference data. The accuracy scores indicate that the ML models can successfully predict reservoir characteristics for marine methane hydrate deposits. The results indicate that the ML models can either augment physics-driven methods for assessing the occurrence and saturation of methane hydrate deposits or serve as an independent predictive tool for those characteristics.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135590499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Women in John’s Gospel","authors":"Susan B Miller","doi":"10.5040/9780567708267","DOIUrl":"https://doi.org/10.5040/9780567708267","url":null,"abstract":"","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47765686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cong Tan, Bingsong Yu, Ce Liu, Zhenglin Cao, He Bi, Hui Jin, Rongjun Yang
The Permian-Triassic in the Ordos Basin deposited a colorful set of strata. Though there have been many studies on oil and gas resources here, little attention has been paid to the genesis mechanism of its color. To analyze the color origin of the Permian-Triassic claystones, and to clarify the dialectical relationship between rock color and sedimentary environment, petrological and geochemical methods including polarization microscope, scan electron microscope, X ray diffractometer and ICP–MS were used. The results show that that minerals with different colors, composition, and content serve as colorants in sedimentary rocks, with the predominant dyeing component in dark claystones being the organic matter, while red beds primarily acquire their color from hematite and other ferric minerals. Additionally, different rock colors correspond to distinct chemical composition variations. The black-gray rocks of the Yanchang and Shihezi formations have high TOC content, Fe 2+ /Fe 3+ , V/Cr, and low CaO/(MgO × Al 2 O 3 ) and Sr/Cu, while the red-brown rocks of the Liujiagou and Heshanggou formations exhibit the opposite. Therefore, the different colors of the rocks stem from their different mineral and chemical compositions, which in turn influenced by the changes of the paleoclimate, paleoenvironment and tectonic movements. Comprehensive analysis of color, mineral and chemical composition reveals the evolution process of paleoclimate and paleoenvironment in the Ordos Basin from the late Permian to Triassic, beginning with a warm, humid climate characterized by a weak oxidation environment in the Shihezi and Shiqianfeng formations, transitioning to a hot, arid climate in the Liujiagou and Heshanggou formations, and returning to a warm, humid climate with a weak oxidation environment in the Zhifang and Yanchang formations. This evolution history aligns with the global tectonic and climate evolution. Overall, Systematic analysis of sedimentary rock color can provide an important basis for the study of the paleoclimate and paleoenvironment.
鄂尔多斯盆地二叠纪-三叠纪沉积了一套色彩丰富的地层。虽然对该区油气资源的研究较多，但对其颜色成因机制的研究较少。运用偏光显微镜、扫描电镜、X射线衍射仪、ICP-MS等岩石学和地球化学方法，分析二叠系—三叠系粘土岩的颜色成因，阐明岩石颜色与沉积环境的辩证关系。结果表明:不同颜色、组成和含量的矿物是沉积岩的着色剂，深色粘土层的染色成分主要是有机质，而红色层的颜色主要来自赤铁矿和其他铁矿物。此外，不同的岩石颜色对应着不同的化学成分变化。延长组和石河子组的黑灰色岩石TOC、fe2 + / fe3 +、V/Cr含量较高，CaO/(MgO × Al 2o3)和Sr/Cu含量较低，而刘家沟组和河上沟组的红棕色岩石则相反。因此，岩石的不同颜色源于其矿物和化学成分的不同，而这些矿物和化学成分又受古气候、古环境变化和构造运动的影响。综合颜色、矿物和化学成分分析，揭示了鄂尔多斯盆地晚二叠世至三叠纪的古气候和古环境演化过程，从石河子组和石千峰组开始，气候温暖湿润，以弱氧化环境为特征，在刘家沟组和河上沟组过渡到炎热干旱气候，再到温暖、干燥的气候。志坊组和延长组气候湿润，氧化环境弱。这一演化历史与全球构造和气候演化一致。总的来说，沉积岩颜色的系统分析可以为研究古气候和古环境提供重要依据。
{"title":"Color origin and its sedimentary and paleoenvironmenta significance of the Permian- Triassic strata in the Ordos Basin, China","authors":"Cong Tan, Bingsong Yu, Ce Liu, Zhenglin Cao, He Bi, Hui Jin, Rongjun Yang","doi":"10.1190/int-2023-0023.1","DOIUrl":"https://doi.org/10.1190/int-2023-0023.1","url":null,"abstract":"The Permian-Triassic in the Ordos Basin deposited a colorful set of strata. Though there have been many studies on oil and gas resources here, little attention has been paid to the genesis mechanism of its color. To analyze the color origin of the Permian-Triassic claystones, and to clarify the dialectical relationship between rock color and sedimentary environment, petrological and geochemical methods including polarization microscope, scan electron microscope, X ray diffractometer and ICP–MS were used. The results show that that minerals with different colors, composition, and content serve as colorants in sedimentary rocks, with the predominant dyeing component in dark claystones being the organic matter, while red beds primarily acquire their color from hematite and other ferric minerals. Additionally, different rock colors correspond to distinct chemical composition variations. The black-gray rocks of the Yanchang and Shihezi formations have high TOC content, Fe 2+ /Fe 3+ , V/Cr, and low CaO/(MgO × Al 2 O 3 ) and Sr/Cu, while the red-brown rocks of the Liujiagou and Heshanggou formations exhibit the opposite. Therefore, the different colors of the rocks stem from their different mineral and chemical compositions, which in turn influenced by the changes of the paleoclimate, paleoenvironment and tectonic movements. Comprehensive analysis of color, mineral and chemical composition reveals the evolution process of paleoclimate and paleoenvironment in the Ordos Basin from the late Permian to Triassic, beginning with a warm, humid climate characterized by a weak oxidation environment in the Shihezi and Shiqianfeng formations, transitioning to a hot, arid climate in the Liujiagou and Heshanggou formations, and returning to a warm, humid climate with a weak oxidation environment in the Zhifang and Yanchang formations. This evolution history aligns with the global tectonic and climate evolution. Overall, Systematic analysis of sedimentary rock color can provide an important basis for the study of the paleoclimate and paleoenvironment.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Delu Li, Haibin Li, Qianyang He, Jianwen Gao, Wenxing Tao, Shimiao Wang
Understanding the mechanical characteristics of marine shale during fracturing is essential for shale gas development, and its core scientific problem is what factors in shale control its mechanical properties. The 12 shale samples from the Lower Paleozoic Niutitang Formation in Micangshan are tested for tensile strength and examined using X-ray diffraction, low-field nuclear magnetic resonance (NMR), EA2000 elemental analyzer, and scanning electron microscopy to explore the main controlling factors of shale tensile strength under horizontal bedding conditions. The findings are as follows. (1) The tensile strength of the shale is relatively high, ranging from 10.05 MPa to 20.34 MPa. Quartz is the largest proportion of the shale minerals, accounting for 53.2 wt%–59.0 wt%, followed by anorthose and clay minerals. Total organic carbon (TOC) concentration ranges from 1.7 wt% to 4.1 wt%. (2) NMR results indicate that the pore structure of shale is mainly mesoporous, accounting for 75.76%–88.03%, followed by macropores (12.57%–21.24%) and micropores (0.68%–4.91%). Low-pressure nitrogen adsorption and desorption results indicate that the average pore diameter of shale is 12.58–16.02 nm, which is basically consistent with NMR results. The negative correlation between fractal dimension D 2 and tensile strength indicates that the higher the tensile strength of the shale, the lower the complexity of its seepage pores. (3) Micropores occur mainly in clay minerals, whereas quartz indicates positively correlation with mesoporous content. The higher the proportion of mesopores, the lower the tensile strength. This indicates that the mesopores are the main factor controlling the tensile strength, and the quartz content in minerals is a secondary factor restricting the tensile strength. TOC has little controlling action on the tensile strength. This contribution provides a theoretical basis for shale fracturing.
{"title":"The main controlling factors of tensile strength in sight of shale reservoir under horizontal bedding: Example of the Lower Paleozoic Niutitang Formation shale from Micangshan, China","authors":"Delu Li, Haibin Li, Qianyang He, Jianwen Gao, Wenxing Tao, Shimiao Wang","doi":"10.1190/int-2023-0047.1","DOIUrl":"https://doi.org/10.1190/int-2023-0047.1","url":null,"abstract":"Understanding the mechanical characteristics of marine shale during fracturing is essential for shale gas development, and its core scientific problem is what factors in shale control its mechanical properties. The 12 shale samples from the Lower Paleozoic Niutitang Formation in Micangshan are tested for tensile strength and examined using X-ray diffraction, low-field nuclear magnetic resonance (NMR), EA2000 elemental analyzer, and scanning electron microscopy to explore the main controlling factors of shale tensile strength under horizontal bedding conditions. The findings are as follows. (1) The tensile strength of the shale is relatively high, ranging from 10.05 MPa to 20.34 MPa. Quartz is the largest proportion of the shale minerals, accounting for 53.2 wt%–59.0 wt%, followed by anorthose and clay minerals. Total organic carbon (TOC) concentration ranges from 1.7 wt% to 4.1 wt%. (2) NMR results indicate that the pore structure of shale is mainly mesoporous, accounting for 75.76%–88.03%, followed by macropores (12.57%–21.24%) and micropores (0.68%–4.91%). Low-pressure nitrogen adsorption and desorption results indicate that the average pore diameter of shale is 12.58–16.02 nm, which is basically consistent with NMR results. The negative correlation between fractal dimension D 2 and tensile strength indicates that the higher the tensile strength of the shale, the lower the complexity of its seepage pores. (3) Micropores occur mainly in clay minerals, whereas quartz indicates positively correlation with mesoporous content. The higher the proportion of mesopores, the lower the tensile strength. This indicates that the mesopores are the main factor controlling the tensile strength, and the quartz content in minerals is a secondary factor restricting the tensile strength. TOC has little controlling action on the tensile strength. This contribution provides a theoretical basis for shale fracturing.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yufei Gong, Chenyang Zhu, Guowei Zhu, Lei Zhang, Guangui Zou
Accurate fault identification in coal mines is important to improve mine safety and economic benefits. We compared various intelligent algorithms for data pre-processing and optimisation, and analysed the construction methods of seismic attribute datasets and the performance of intelligent optimisation algorithms using fault identification accuracy as the discrimination index to find a better combined model for seismic fault identification. First, the training dataset is constructed by mining the fault and non-fault information revealed by the roadway. The distribution characteristics of the seismic attribute data show similarities among them, and they are non-linearly separable. Directly using the attributes to construct the dataset, the accuracy of fault identification using the support vector machine model was 78.41%. Principal Component Analysis and Self-Organising Mapping Neural Network were used to extract effective information, and then combined with the SVM classification model, the accuracy of fault identification was 83.82% and 87.47%, respectively. Compared with the original data and PCA dimensionality reduction data, the accuracy of fault detection is improved by 9.06% and 3.66%, respectively, indicating that SOM can effectively improve the accuracy of fault detection by eliminating similar attributes and reducing the weight of redundant information. Then, through fixed attribute data set, Genetic Algorithm, Particle Swarm Optimization and Grey Wolf Optimizer intelligent optimization algorithms were used to find the optimal kernel function parameter and penalty parameter of SVM classifier, the accuracy rate of SOM-GWO-SVM model reached 91.12%, compared with SOM-PSO-SVM and SOM-GA-SVM, the model accuracy is increased by 5.2% and 5.61%, respectively. Compared with PSO and GA, the GWO algorithm has a better global search ability. The identification result of the SOM-GWO-SVM model is closest to the actual fault exposure, especially for the identification of "short" faults and associated faults, which has obvious advantages over the traditional manual interpretation in terms of efficiency and accuracy.
{"title":"Seismic fault identification in coal mines based on SOMGWOSVM algorithm","authors":"Yufei Gong, Chenyang Zhu, Guowei Zhu, Lei Zhang, Guangui Zou","doi":"10.1190/int-2023-0025.1","DOIUrl":"https://doi.org/10.1190/int-2023-0025.1","url":null,"abstract":"Accurate fault identification in coal mines is important to improve mine safety and economic benefits. We compared various intelligent algorithms for data pre-processing and optimisation, and analysed the construction methods of seismic attribute datasets and the performance of intelligent optimisation algorithms using fault identification accuracy as the discrimination index to find a better combined model for seismic fault identification. First, the training dataset is constructed by mining the fault and non-fault information revealed by the roadway. The distribution characteristics of the seismic attribute data show similarities among them, and they are non-linearly separable. Directly using the attributes to construct the dataset, the accuracy of fault identification using the support vector machine model was 78.41%. Principal Component Analysis and Self-Organising Mapping Neural Network were used to extract effective information, and then combined with the SVM classification model, the accuracy of fault identification was 83.82% and 87.47%, respectively. Compared with the original data and PCA dimensionality reduction data, the accuracy of fault detection is improved by 9.06% and 3.66%, respectively, indicating that SOM can effectively improve the accuracy of fault detection by eliminating similar attributes and reducing the weight of redundant information. Then, through fixed attribute data set, Genetic Algorithm, Particle Swarm Optimization and Grey Wolf Optimizer intelligent optimization algorithms were used to find the optimal kernel function parameter and penalty parameter of SVM classifier, the accuracy rate of SOM-GWO-SVM model reached 91.12%, compared with SOM-PSO-SVM and SOM-GA-SVM, the model accuracy is increased by 5.2% and 5.61%, respectively. Compared with PSO and GA, the GWO algorithm has a better global search ability. The identification result of the SOM-GWO-SVM model is closest to the actual fault exposure, especially for the identification of \"short\" faults and associated faults, which has obvious advantages over the traditional manual interpretation in terms of efficiency and accuracy.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47260293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Tschache, V. Vinje, Jan Erik Lie, Martin Brandtzæg Gundem, Einar Iversen
Net-to-gross ratio and net pay are essential properties for characterizing turbidite reservoirs. We present a Bayesian inversion that estimates the probability density distributions of the reservoir properties from the amplitude-variation-with-offset (AVO) attributes intercept and gradient, which are measured at the top of the reservoir. The method is adapted to the region-specific characteristics of the sand-shale interbedding as observed from well data. The likelihood function is estimated by a Monte Carlo simulation, which involves generating pseudo-wells, seismic modeling using the reflectivity method, picking the amplitudes at the top of the reservoir, and estimating the AVO intercept and gradient. In a North Sea oil field case example, the AVO gradient is most sensitive to variations in the net-to-gross ratio, while the AVO intercept is most sensitive to the type of pore fluid. The inversion was successfully tested on pseudo-wells and synthetic seismic AVO from well data. We show that the inversion can be applied to AVO maps to produce maps of the most likely estimates of the net-to-gross ratio and the net pay-to-net ratio, the resulting net pay, and the uncertainty.
{"title":"Estimation of net-to-gross ratio and net pay from seismic amplitude variation with offset using Bayesian inversion","authors":"S. Tschache, V. Vinje, Jan Erik Lie, Martin Brandtzæg Gundem, Einar Iversen","doi":"10.1190/int-2023-0034.1","DOIUrl":"https://doi.org/10.1190/int-2023-0034.1","url":null,"abstract":"Net-to-gross ratio and net pay are essential properties for characterizing turbidite reservoirs. We present a Bayesian inversion that estimates the probability density distributions of the reservoir properties from the amplitude-variation-with-offset (AVO) attributes intercept and gradient, which are measured at the top of the reservoir. The method is adapted to the region-specific characteristics of the sand-shale interbedding as observed from well data. The likelihood function is estimated by a Monte Carlo simulation, which involves generating pseudo-wells, seismic modeling using the reflectivity method, picking the amplitudes at the top of the reservoir, and estimating the AVO intercept and gradient. In a North Sea oil field case example, the AVO gradient is most sensitive to variations in the net-to-gross ratio, while the AVO intercept is most sensitive to the type of pore fluid. The inversion was successfully tested on pseudo-wells and synthetic seismic AVO from well data. We show that the inversion can be applied to AVO maps to produce maps of the most likely estimates of the net-to-gross ratio and the net pay-to-net ratio, the resulting net pay, and the uncertainty.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48346205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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