Naihao Liu, Youbo Lei, Yang Yang, Zhiguo Wang, Rongchang Liu, Jinghuai Gao, Tao Wei
Time-frequency (TF) analysis is commonly used to reveal the local properties of seismic signals, such as frequency and spectral contents varying with time/depth. Aiming to realize a highly localized TF representation of seismic signals, researchers treated the TF analysis as an inverse problem, and regularization was adopted in the objective functions. Traditionally, the TF sparse inversion process is solved by the Lasso regression. It has been proven that the Lasso regression needs a large number of iterations to reach a high accurate solution for the convex problem. Recently, convolutional neural networks (CNNs) have been successfully used to solve the convex problem due to their high computational efficiency and strong nonlinear characterization ability. We propose to solve the sparse TF inversion using CNN and our method is named STFA-CNN. The objective function in the neural network architecture consists of two portions. The first one is to minimize the difference between the local forward and backward Fourier transform of seismic signals. The second is minimizing the regularization l p norm of TF results. To demonstrate the effectiveness of our method, we apply it to both synthetic and real seismic data. We further use the TF results to compute the attenuation of seismic waveforms and apply the attenuation attribute to predict the hydrocarbons of a seismic survey acquired over the Ordos Basin, Northwest of China.
{"title":"Sparse Time-Frequency Analysis of Seismic Data via Convolutional Neural Network","authors":"Naihao Liu, Youbo Lei, Yang Yang, Zhiguo Wang, Rongchang Liu, Jinghuai Gao, Tao Wei","doi":"10.1190/int-2023-0020.1","DOIUrl":"https://doi.org/10.1190/int-2023-0020.1","url":null,"abstract":"Time-frequency (TF) analysis is commonly used to reveal the local properties of seismic signals, such as frequency and spectral contents varying with time/depth. Aiming to realize a highly localized TF representation of seismic signals, researchers treated the TF analysis as an inverse problem, and regularization was adopted in the objective functions. Traditionally, the TF sparse inversion process is solved by the Lasso regression. It has been proven that the Lasso regression needs a large number of iterations to reach a high accurate solution for the convex problem. Recently, convolutional neural networks (CNNs) have been successfully used to solve the convex problem due to their high computational efficiency and strong nonlinear characterization ability. We propose to solve the sparse TF inversion using CNN and our method is named STFA-CNN. The objective function in the neural network architecture consists of two portions. The first one is to minimize the difference between the local forward and backward Fourier transform of seismic signals. The second is minimizing the regularization l p norm of TF results. To demonstrate the effectiveness of our method, we apply it to both synthetic and real seismic data. We further use the TF results to compute the attenuation of seismic waveforms and apply the attenuation attribute to predict the hydrocarbons of a seismic survey acquired over the Ordos Basin, Northwest of China.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48102220","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 prediction and evaluation of in-situ stress field plays an important role in many engineering fields. How to accurately obtain in-situ stress field information of a large area becomes a focus in geophysics. Wide azimuth seismic data establish a bridge between in-situ stress and rock anisotropy, making it possible to predict the large-scale in-situ stress field. Ellipse fitting is a common method to predict in-situ stress according to the characteristics of seismic attribute change with azimuth, but there are some problems such as 90° ambiguity in orientation prediction and the unclear stress-related physical meaning of the fitting parameters. Moreover, the variation of azimuthal seismic attribute in strongly anisotropic media does not meet ellipse hypothesis, which also limits the application of ellipse fitting method. Through mathematical simulation experiment, the mechanism of seismic response characteristics under orthotropic stress situation is explored. Focus on the property of strong anisotropy induced by in-situ stress in subsurface media, a new stress circle fitting method for in-situ stress prediction is established by combining the azimuthal variation characteristics of reflection coefficient with the Mohr circle theory. The fitting results have clear physical significance related to in-situ stress. Besides, through analysis of fitting parameters, the influence of 90o ambiguity problem can be eliminated. Ellipse fitting method and stress circle fitting method are applied to actual wide-azimuth seismic data. Comparison shows that the stress circle fitting result is more suitable for azimuth seismic data in strongly anisotropic media. Compared with ellipse fitting, in-situ stress field distribution predicted by stress circle fitting method is more reasonable. The actual imaging logging results also prove the accuracy of stress circle fitting method.
{"title":"In-situ stress field detection of stress-induced strong anisotropy media based on Mohr circle theory","authors":"Jing-ya Yang, Fanchang Zhang, Xunyong Xu","doi":"10.1190/int-2023-0024.1","DOIUrl":"https://doi.org/10.1190/int-2023-0024.1","url":null,"abstract":"The prediction and evaluation of in-situ stress field plays an important role in many engineering fields. How to accurately obtain in-situ stress field information of a large area becomes a focus in geophysics. Wide azimuth seismic data establish a bridge between in-situ stress and rock anisotropy, making it possible to predict the large-scale in-situ stress field. Ellipse fitting is a common method to predict in-situ stress according to the characteristics of seismic attribute change with azimuth, but there are some problems such as 90° ambiguity in orientation prediction and the unclear stress-related physical meaning of the fitting parameters. Moreover, the variation of azimuthal seismic attribute in strongly anisotropic media does not meet ellipse hypothesis, which also limits the application of ellipse fitting method. Through mathematical simulation experiment, the mechanism of seismic response characteristics under orthotropic stress situation is explored. Focus on the property of strong anisotropy induced by in-situ stress in subsurface media, a new stress circle fitting method for in-situ stress prediction is established by combining the azimuthal variation characteristics of reflection coefficient with the Mohr circle theory. The fitting results have clear physical significance related to in-situ stress. Besides, through analysis of fitting parameters, the influence of 90o ambiguity problem can be eliminated. Ellipse fitting method and stress circle fitting method are applied to actual wide-azimuth seismic data. Comparison shows that the stress circle fitting result is more suitable for azimuth seismic data in strongly anisotropic media. Compared with ellipse fitting, in-situ stress field distribution predicted by stress circle fitting method is more reasonable. The actual imaging logging results also prove the accuracy of stress circle fitting method.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":"58 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66168442","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}
Shohei Sakaida, Yasuyuki Hamanaka, D. Zhu, A. Hill
Multistage hydraulic fracturing design on horizontal wells has significantly evolved with larger fluid volume, more fracturing stages, and tighter perforation cluster spacing to efficiently stimulate unconventional reservoirs. From the published field observations, the recent fracturing design results in complex fracture networks or swarm of fractures. Fracture treatment evaluation is extremely challenging in such a case, because of the large amount of variables in well completion and stimulation design. Combined measurements from different technologies can help in fracture diagnosis. Fluid distribution, either during fracture injection or during production, directly relates to the stimulation efficiency at the cluster level, and at the stage level. Since it is unlikely in the real world to distribute the injected fluid uniformly among all the clusters, we need diagnostic techniques to generate the flow profile along a lateral. Fiber optic measurements including Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) are currently used to diagnose downhole flow conditions. This technology allows us to qualitatively confirm the fluid flow profile and other issues occurring downhole during fracturing such as leakage through plugs. For optimizing a fracturing design, we also need to understand how the design parameters are correlated with the stimulation efficiency. In this study, we combine the two sets of models of DAS and DTS data interpretation for injected fluid volume distribution. DAS is interpreted based on an empirical correlation between fluid flow rates and frequency band energy from the acoustic signals. DTS is interpreted by performing temperature history match based thermal energy conservation. Because of the completely different physics behind the interpretations, the confirmation of the two interpretations provides confidence in fluid distribution.
{"title":"DEVELOPMENT OF FRACTURE DIAGNOSTIC METHODS FOR FLUID DISTRIBUTION BASED ON QUANTITATIVE INTERPRETATION OF DAS AND DTS","authors":"Shohei Sakaida, Yasuyuki Hamanaka, D. Zhu, A. Hill","doi":"10.1190/int-2022-0099.1","DOIUrl":"https://doi.org/10.1190/int-2022-0099.1","url":null,"abstract":"Multistage hydraulic fracturing design on horizontal wells has significantly evolved with larger fluid volume, more fracturing stages, and tighter perforation cluster spacing to efficiently stimulate unconventional reservoirs. From the published field observations, the recent fracturing design results in complex fracture networks or swarm of fractures. Fracture treatment evaluation is extremely challenging in such a case, because of the large amount of variables in well completion and stimulation design. Combined measurements from different technologies can help in fracture diagnosis. Fluid distribution, either during fracture injection or during production, directly relates to the stimulation efficiency at the cluster level, and at the stage level. Since it is unlikely in the real world to distribute the injected fluid uniformly among all the clusters, we need diagnostic techniques to generate the flow profile along a lateral. Fiber optic measurements including Distributed Acoustic Sensing (DAS) and Distributed Temperature Sensing (DTS) are currently used to diagnose downhole flow conditions. This technology allows us to qualitatively confirm the fluid flow profile and other issues occurring downhole during fracturing such as leakage through plugs. For optimizing a fracturing design, we also need to understand how the design parameters are correlated with the stimulation efficiency. In this study, we combine the two sets of models of DAS and DTS data interpretation for injected fluid volume distribution. DAS is interpreted based on an empirical correlation between fluid flow rates and frequency band energy from the acoustic signals. DTS is interpreted by performing temperature history match based thermal energy conservation. Because of the completely different physics behind the interpretations, the confirmation of the two interpretations provides confidence in fluid distribution.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43756312","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}
T. Larson, J. E. Sivil, Priyank Periwal, J. Melick
Characterization of subsurface reservoirs that are dominated by mudrock facies is hindered by the inherent heterogeneity and high degree of spatial variability typical of mudrock depositional systems. Subsurface reservoir properties that include porosity and permeability, fluid saturations, stratigraphic thicknesses of reservoir units, and source rock potential are ultimately controlled by the spatial distribution of sedimentary rock facies, which supports efforts to improve subsurface characterization workflows. Although core-based data provide direct measurements of rock attributes that are used to inform static reservoir models, capturing high-resolution core-based rock facies and downscaling these observations to tie to lower-resolution wireline logs remains a challenge. The effort to integrate core-based facies to reservoir-scale models is especially difficult when trying to capture thin-bedded heterogeneity that is common to mudrock systems. Herein a workflow is developed and applied to visualize and integrate multivariate and spatially complex core-based datasets with wireline logs. Formation-specific core-based chemofacies training datasets are developed by integrating core descriptions with chemofacies clusters developed from high-resolution X-ray fluorescence core scanning. Core-based rock attribute data (e.g., X-ray diffraction mineralogy, total porosity, and total organic matter content) are used to describe the chemofacies, providing a means to upscale low-resolution rock attribute measurements to high-resolution core-based chemofacies. Supervised core-based chemofacies training datasets are then used with neural network multi-class classification machine learning tools to train triple combo wireline logs (gamma ray, deep resistivity, bulk density, and neutron porosity) to predict rock facies from wireline logs, providing a new approach to apply core-based facies classifications to wireline log studies. A basin-scale case study that applies this work flow is described for the Third Bone Spring Sand and units of the Wolfcamp Formation in the Delaware Basin of West Texas, United States.
{"title":"A machine learning workflow to integrate high-resolution core-based facies into basin-scale stratigraphic models for the Wolfcamp and Third Bone Spring Sand, Delaware Basin","authors":"T. Larson, J. E. Sivil, Priyank Periwal, J. Melick","doi":"10.1190/int-2023-0009.1","DOIUrl":"https://doi.org/10.1190/int-2023-0009.1","url":null,"abstract":"Characterization of subsurface reservoirs that are dominated by mudrock facies is hindered by the inherent heterogeneity and high degree of spatial variability typical of mudrock depositional systems. Subsurface reservoir properties that include porosity and permeability, fluid saturations, stratigraphic thicknesses of reservoir units, and source rock potential are ultimately controlled by the spatial distribution of sedimentary rock facies, which supports efforts to improve subsurface characterization workflows. Although core-based data provide direct measurements of rock attributes that are used to inform static reservoir models, capturing high-resolution core-based rock facies and downscaling these observations to tie to lower-resolution wireline logs remains a challenge. The effort to integrate core-based facies to reservoir-scale models is especially difficult when trying to capture thin-bedded heterogeneity that is common to mudrock systems. Herein a workflow is developed and applied to visualize and integrate multivariate and spatially complex core-based datasets with wireline logs. Formation-specific core-based chemofacies training datasets are developed by integrating core descriptions with chemofacies clusters developed from high-resolution X-ray fluorescence core scanning. Core-based rock attribute data (e.g., X-ray diffraction mineralogy, total porosity, and total organic matter content) are used to describe the chemofacies, providing a means to upscale low-resolution rock attribute measurements to high-resolution core-based chemofacies. Supervised core-based chemofacies training datasets are then used with neural network multi-class classification machine learning tools to train triple combo wireline logs (gamma ray, deep resistivity, bulk density, and neutron porosity) to predict rock facies from wireline logs, providing a new approach to apply core-based facies classifications to wireline log studies. A basin-scale case study that applies this work flow is described for the Third Bone Spring Sand and units of the Wolfcamp Formation in the Delaware Basin of West Texas, United States.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47042631","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}
J. Liu, Wei-dong Gong, Peng Wang, Yingjun Yang, Jun You
The central Tarim Basin has garnered significant attention due to its petroleum reserves, particularly the recent commercial discovery of Ordovician-age carbonate reservoirs in the Shunbei Oil and Gas Field. This study presents a systematic analysis based on 3D seismic interpretation in the central Tarim Basin. The results reveal the presence of several major strike-slip faults and associated fracture systems. The characteristics of these major strike-slip faults exhibit a lower positive or negative flower structure in the Lower-Middle Ordovician, while faults in the Upper Ordovician display a normal sense of movement attributed to the regional stress field. Furthermore, these major strike-slip faults commonly give rise to fractures at various scales. The fracture systems in different segments of the major strike-slip faults exhibit notable differences in their characteristics. Additionally, two models are proposed to describe strike-slip fault-associated fracture systems in both compressional and extensional settings. The development of fracture systems is highly variable and depends on the scale of the strike-slip fractured zone. In small-scale shear zones or strike-slip fractured zones, the fracture systems typically develop along the fracture plane. Conversely, in large-scale strike-slip fractured zones, the fracture systems commonly develop along the fault zone on both sides.
{"title":"The controls of strike-slip faults on fracture systems: insights from 3D seismic data in central Tarim Basin, NW China","authors":"J. Liu, Wei-dong Gong, Peng Wang, Yingjun Yang, Jun You","doi":"10.1190/int-2022-0121.1","DOIUrl":"https://doi.org/10.1190/int-2022-0121.1","url":null,"abstract":"The central Tarim Basin has garnered significant attention due to its petroleum reserves, particularly the recent commercial discovery of Ordovician-age carbonate reservoirs in the Shunbei Oil and Gas Field. This study presents a systematic analysis based on 3D seismic interpretation in the central Tarim Basin. The results reveal the presence of several major strike-slip faults and associated fracture systems. The characteristics of these major strike-slip faults exhibit a lower positive or negative flower structure in the Lower-Middle Ordovician, while faults in the Upper Ordovician display a normal sense of movement attributed to the regional stress field. Furthermore, these major strike-slip faults commonly give rise to fractures at various scales. The fracture systems in different segments of the major strike-slip faults exhibit notable differences in their characteristics. Additionally, two models are proposed to describe strike-slip fault-associated fracture systems in both compressional and extensional settings. The development of fracture systems is highly variable and depends on the scale of the strike-slip fractured zone. In small-scale shear zones or strike-slip fractured zones, the fracture systems typically develop along the fracture plane. Conversely, in large-scale strike-slip fractured zones, the fracture systems commonly develop along the fault zone on both sides.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49206565","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}
L. S. B. Oliveira, B. Alaei, A. Torabi, K. M. L. Oliveira, D. L. Vasconcelos, F. Bezerra, F. Nogueira
Seismic interpretation is crucial for identifying faults, fluid concentrations, and flow migration pathways in the oil and gas industry. Algorithms have been developed to identify faults using seismic data and attributes such as changes in amplitude, phase, polarity, and frequency. Despite technological advancements, challenges remain in seismic interpretation due to noise, quality of data, and fault dimensions. Deep learning has recently been applied to image faults from seismic data, making the process faster and more reliable. This paper evaluates the performance of Deep Neural Networks (DNN) in fault interpretation by comparing the results with traditional seismic attributes in onshore seismic data. Our results indicate that the DNN reveals more structural detail, which is essential in characterizing the 3D fault geometry. In addition, DNN results show better continuity, fewer false positives, and are less affected by noise in the onshore seismic data used in this case. The 3D fault model from DNN identifies faults and their fault segments with greater variability of strikes and reveals more minor faults. Based on the DNN fault model, we characterized the 3D geometry of a new fault in the Rio do Peixe Basin without noise influence.
地震解释对于识别石油和天然气行业中的断层、流体浓度和流动迁移路径至关重要。已经开发了使用地震数据和属性（如振幅、相位、极性和频率的变化）来识别断层的算法。尽管技术进步，但由于噪声、数据质量和断层尺寸的原因，地震解释仍然面临挑战。深度学习最近被应用于地震数据中的断层图像，使这一过程更快、更可靠。本文通过将深度神经网络（DNN）的结果与陆上地震数据中的传统地震属性进行比较，来评估其在断层解释中的性能。我们的结果表明，DNN揭示了更多的结构细节，这对于表征三维断层几何形状至关重要。此外，在这种情况下使用的陆上地震数据中，DNN结果显示出更好的连续性、更少的假阳性，并且受噪声的影响更小。DNN的三维断层模型识别了走向变化较大的断层及其断层段，并揭示了更多的小断层。基于DNN断层模型，我们在没有噪声影响的情况下，对Rio do Peixe盆地一条新断层的三维几何形状进行了表征。
{"title":"#xD;AUTOMATIC 3D FAULT DETECTION AND CHARACTERIZATION A COMPARISON BETWEEN SEISMIC ATTRIBUTE METHODS AND DEEP LEARNING#xD;","authors":"L. S. B. Oliveira, B. Alaei, A. Torabi, K. M. L. Oliveira, D. L. Vasconcelos, F. Bezerra, F. Nogueira","doi":"10.1190/int-2023-0016.1","DOIUrl":"https://doi.org/10.1190/int-2023-0016.1","url":null,"abstract":"Seismic interpretation is crucial for identifying faults, fluid concentrations, and flow migration pathways in the oil and gas industry. Algorithms have been developed to identify faults using seismic data and attributes such as changes in amplitude, phase, polarity, and frequency. Despite technological advancements, challenges remain in seismic interpretation due to noise, quality of data, and fault dimensions. Deep learning has recently been applied to image faults from seismic data, making the process faster and more reliable. This paper evaluates the performance of Deep Neural Networks (DNN) in fault interpretation by comparing the results with traditional seismic attributes in onshore seismic data. Our results indicate that the DNN reveals more structural detail, which is essential in characterizing the 3D fault geometry. In addition, DNN results show better continuity, fewer false positives, and are less affected by noise in the onshore seismic data used in this case. The 3D fault model from DNN identifies faults and their fault segments with greater variability of strikes and reveals more minor faults. Based on the DNN fault model, we characterized the 3D geometry of a new fault in the Rio do Peixe Basin without noise influence.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42525814","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}
Dewei Li, Jingjing Zheng, S. Peng, Ruizhao Yang, Lingbin Meng, Weijiang Shi
Hydraulic fracturing-induced microseismicity has drawn public attention in recent years. However, understanding the behavior of the hydraulic fracture is limited due to complex relationship between the microseismicity and the various geological conditions. To further understand this question, we conducted a study to detect and locate hydraulic fracturing-induced microseismicity at a shale gas production site in Tongren, Guizhou, China. We investigate the relationship between their distribution and two important geological factors: the brittleness index of rocks and the distribution of natural fractures. With the aid of a 3D active seismic survey, we first calculate the brittleness index of rocks in the hydraulic fracturing region using Youngs modulus and Poissons ratio, compared with the locating result of fracturing-induced microseismicity shows the mostly events distributed in the area with higher brittleness index. We then delineate natural fractures using the Ant Tracking method of the 3D seismic attribute. The microseismic location is consistent with the region of the natural fractures. Based on our findings, we suggest the spatial distribution of induced microseismicity is highly controlled by the brittleness of rocks and the distribution of natural fractures in this region. This research provides insights into the factors controlling hydraulic fracturing-induced microseismicity and enhances our understanding of the complex interplay between geological conditions and the behavior of hydraulic fractures.
{"title":"Hydraulic Fracturing-Induced Microseismicity Controlled by Rock Brittleness and Natural Fractures in Tongren, Guizhou, China","authors":"Dewei Li, Jingjing Zheng, S. Peng, Ruizhao Yang, Lingbin Meng, Weijiang Shi","doi":"10.1190/int-2022-0126.1","DOIUrl":"https://doi.org/10.1190/int-2022-0126.1","url":null,"abstract":"Hydraulic fracturing-induced microseismicity has drawn public attention in recent years. However, understanding the behavior of the hydraulic fracture is limited due to complex relationship between the microseismicity and the various geological conditions. To further understand this question, we conducted a study to detect and locate hydraulic fracturing-induced microseismicity at a shale gas production site in Tongren, Guizhou, China. We investigate the relationship between their distribution and two important geological factors: the brittleness index of rocks and the distribution of natural fractures. With the aid of a 3D active seismic survey, we first calculate the brittleness index of rocks in the hydraulic fracturing region using Youngs modulus and Poissons ratio, compared with the locating result of fracturing-induced microseismicity shows the mostly events distributed in the area with higher brittleness index. We then delineate natural fractures using the Ant Tracking method of the 3D seismic attribute. The microseismic location is consistent with the region of the natural fractures. Based on our findings, we suggest the spatial distribution of induced microseismicity is highly controlled by the brittleness of rocks and the distribution of natural fractures in this region. This research provides insights into the factors controlling hydraulic fracturing-induced microseismicity and enhances our understanding of the complex interplay between geological conditions and the behavior of hydraulic fractures.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43264901","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}
For seismic wave propagation in HTI media, both kinematic and dynamic attributes are anisotropic. P-waves run slower in the direction perpendicular to fracture azimuth than in the direction parallel to fracture azimuth; meanwhile, reflection strength and frequency vary with azimuth. We quantitatively analyzed azimuthal effects of reflection coefficients and velocities of seismic waves in HTI medium. The anisotropy on the azimuth gathers from theory and real wide-azimuth data was studied as well. Therefore, ellipse fitting was performed to quantitatively predict the direction and strength of the anisotropy in the study area, which was consistent with that obtained by the shear wave splitting prediction method. In wide azimuth data processing, in order to eliminate the influence of azimuthal anisotropy, the coherent spectrum pickup method was utilized to accurately calculate the azimuthal velocity of underground HTI media, and conducts azimuthal anisotropy correction processing, which eliminates the fast and slow wave time difference caused by azimuthal anisotropy, and achieves good results, providing a support for subsequent high-resolution imaging.
{"title":"Azimuth Anisotropy Prediction and Correction of Wide-Azimuth Seismic","authors":"Liyan Zhang, Ang Li, Xi Nianxu","doi":"10.1190/int-2022-0112.1","DOIUrl":"https://doi.org/10.1190/int-2022-0112.1","url":null,"abstract":"For seismic wave propagation in HTI media, both kinematic and dynamic attributes are anisotropic. P-waves run slower in the direction perpendicular to fracture azimuth than in the direction parallel to fracture azimuth; meanwhile, reflection strength and frequency vary with azimuth. We quantitatively analyzed azimuthal effects of reflection coefficients and velocities of seismic waves in HTI medium. The anisotropy on the azimuth gathers from theory and real wide-azimuth data was studied as well. Therefore, ellipse fitting was performed to quantitatively predict the direction and strength of the anisotropy in the study area, which was consistent with that obtained by the shear wave splitting prediction method. In wide azimuth data processing, in order to eliminate the influence of azimuthal anisotropy, the coherent spectrum pickup method was utilized to accurately calculate the azimuthal velocity of underground HTI media, and conducts azimuthal anisotropy correction processing, which eliminates the fast and slow wave time difference caused by azimuthal anisotropy, and achieves good results, providing a support for subsequent high-resolution imaging.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42478765","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 coal component content is an important parameter during the coal resources exploration and exploitation. Previous logging curve regression and single neural network methods have the disadvantages of low accuracy and weak generalization ability in calculating coal component content. In this study, a GRA-HNN method was proposed by combining grey relational analysis (GRA) and hybrid neural network (HNN) to predict coal component content in logging data. First, the correlation degree between different conventional logging data and coal components was calculated using the GRA method, and logging curves with a correlation degree = 0.7 were selected as the input training data set. Then, a back propagation neural network (BPNN), support vector machine (SVM) neural network, and radial basis function (RBF) neural network of different coal components were constructed based on the selected optimal input logging data, and the weighted average strategy was used to form a HNN prediction model. Finally, the GRA-HNN method was used to predict the coal component content of coalbed methane production wells in Panji mining area. The application results showed that the coal component content predicted by the GRA-HNN method has the highest accuracy compared to the logging curve regression method and its single neural network model, with a maximum average relative error of 13.4%. Besides, the accuracy of coal component content predicted by some single intelligent models is not always higher than the logging curve regression method, indicating that the neural network model is not necessarily suitable for all coal component content predictions. The proposed GRA-HNN method not only optimizes the prediction performance of a single neural network model by selecting effective input parameters, but also comprehensively considers the prediction effect of several neural network models, which strengthens the generalization ability of neural network model and increases the log interpretation accuracy of coal component content.
{"title":"Interpreting coal component content in logging data by combining grey relational analysis and hybrid neural network","authors":"Ze Bai, Qinjie Liu, M. Tan, Yang Bai, Haibo Wu","doi":"10.1190/int-2022-0077.1","DOIUrl":"https://doi.org/10.1190/int-2022-0077.1","url":null,"abstract":"The coal component content is an important parameter during the coal resources exploration and exploitation. Previous logging curve regression and single neural network methods have the disadvantages of low accuracy and weak generalization ability in calculating coal component content. In this study, a GRA-HNN method was proposed by combining grey relational analysis (GRA) and hybrid neural network (HNN) to predict coal component content in logging data. First, the correlation degree between different conventional logging data and coal components was calculated using the GRA method, and logging curves with a correlation degree = 0.7 were selected as the input training data set. Then, a back propagation neural network (BPNN), support vector machine (SVM) neural network, and radial basis function (RBF) neural network of different coal components were constructed based on the selected optimal input logging data, and the weighted average strategy was used to form a HNN prediction model. Finally, the GRA-HNN method was used to predict the coal component content of coalbed methane production wells in Panji mining area. The application results showed that the coal component content predicted by the GRA-HNN method has the highest accuracy compared to the logging curve regression method and its single neural network model, with a maximum average relative error of 13.4%. Besides, the accuracy of coal component content predicted by some single intelligent models is not always higher than the logging curve regression method, indicating that the neural network model is not necessarily suitable for all coal component content predictions. The proposed GRA-HNN method not only optimizes the prediction performance of a single neural network model by selecting effective input parameters, but also comprehensively considers the prediction effect of several neural network models, which strengthens the generalization ability of neural network model and increases the log interpretation accuracy of coal component content.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41387871","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}
Solvent extracts obtained from center-cut horizontal core plugs selected in Upper Wolfcamp (UW) and Eagle Ford source-rock (SR) beds contain unaltered volatile (i.e., gasoline-range) HC compounds because they were extracted in a closed vial. Therefore, a C7 source parameter, a C7 maturity parameter, and pristane/phytane ratios were used to compare the source and thermal maturity of these petroleum samples and oil samples produced from nearby wells landed in the same SR reservoirs. Five distinct pay zones previously identified in the UW SR reservoir using geological criteria each contain slightly different kinds of petroleum generated at different levels of thermal maturity. A thick overlying carbonate reservoir contains the kind of petroleum generated by the kerogen present in one underlying SR pay zone. The same source and maturity parameters demonstrate that the oil-prone kerogen present in Eagle Ford SR beds in core plugs selected from wells located 7.5 mi (12 km) apart on the San Marcos Arch in South Texas formed in different depositional environments. It is difficult to allocate commingled oil samples using only core-plug extracts because solvents extract the producible oil plus a component that does not readily flow from SR reservoirs because it is sorbed in kerogen and/or on clay minerals. However, because only saturate HC compounds were used to determine C7 source and maturity parameters, they provide valuable insights about the nature of the free oil present in SR reservoirs and in commingled oil samples.
从Upper Wolfcamp（UW）和Eagle Ford源岩（SR）床中选择的中心切割水平岩心塞中获得的溶剂提取物含有未改变的挥发性（即汽油范围）HC化合物，因为它们是在封闭的小瓶中提取的。因此，使用C7来源参数、C7成熟度参数和三烷/植烷比率来比较这些石油样品与同一SR储层中附近油井生产的石油样品的来源和热成熟度。之前根据地质标准在UW SR储层中确定的五个不同的产油层，每个产油层都包含在不同热成熟度水平下产生的略有不同的石油种类。厚厚的上覆碳酸盐岩储层包含由下伏SR产层中的干酪根产生的石油。相同的来源和成熟度参数表明，Eagle Ford SR层中的易油干酪根在不同的沉积环境中形成，这些岩芯塞选自德克萨斯州南部圣马科斯拱门上相距7.5英里（12公里）的油井。仅使用岩心塞提取物很难分配混合油样，因为溶剂提取可生产的油加上一种组分，该组分由于吸附在干酪根和/或粘土矿物中而不容易从SR储层中流出。然而，由于仅使用饱和HC化合物来确定C7来源和成熟度参数，因此它们为SR油藏和混合油样品中游离油的性质提供了有价值的见解。
{"title":"Characterizing petroleum in source-rock core samples using HRGC data","authors":"A. Kornacki","doi":"10.1190/int-2023-0003.1","DOIUrl":"https://doi.org/10.1190/int-2023-0003.1","url":null,"abstract":"Solvent extracts obtained from center-cut horizontal core plugs selected in Upper Wolfcamp (UW) and Eagle Ford source-rock (SR) beds contain unaltered volatile (i.e., gasoline-range) HC compounds because they were extracted in a closed vial. Therefore, a C7 source parameter, a C7 maturity parameter, and pristane/phytane ratios were used to compare the source and thermal maturity of these petroleum samples and oil samples produced from nearby wells landed in the same SR reservoirs. Five distinct pay zones previously identified in the UW SR reservoir using geological criteria each contain slightly different kinds of petroleum generated at different levels of thermal maturity. A thick overlying carbonate reservoir contains the kind of petroleum generated by the kerogen present in one underlying SR pay zone. The same source and maturity parameters demonstrate that the oil-prone kerogen present in Eagle Ford SR beds in core plugs selected from wells located 7.5 mi (12 km) apart on the San Marcos Arch in South Texas formed in different depositional environments. It is difficult to allocate commingled oil samples using only core-plug extracts because solvents extract the producible oil plus a component that does not readily flow from SR reservoirs because it is sorbed in kerogen and/or on clay minerals. However, because only saturate HC compounds were used to determine C7 source and maturity parameters, they provide valuable insights about the nature of the free oil present in SR reservoirs and in commingled oil samples.","PeriodicalId":51318,"journal":{"name":"Interpretation-A Journal of Subsurface Characterization","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41645020","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：