Hydraulic fracturing, a significant contributor to seismic activity within and around operational fields, has been extensively employed in shale gas production. Magnetotelluric Sounding (MT) as an effective geophysical tool for identifying high-conductivity fluid-filled and/or molten regions. In this study, we deploy a dense grid of rectangular MT sites to investigate the three-dimensional (3-D) geoelectrical resistivity structure beneath the Weiyuan shale gas block (WSGB) and subsequently examine the causes of seismic activity. The resistivity data, obtained through 3-D inversion accounting for topography using ModEM, reveals a shallow low-resistivity layer (< 10 Ω-m) within the WSGB, ranging from approximately 2 to 5 km in depth. This layer exhibits multiple isolated areas with very low resistivity (< 5 Ω-m), indicative of fluid-filled zones associated with hydraulic fracturing or shale gas-bearing formations. In the northwestern WSGB, the Weiyuan anticline presents a high-resistivity dome extending possibly to depths beyond 20 km, without extending beyond the northern boundary of our study area. Conversely, the sedimentary zone in the southeastern WSGB displays a low-resistivity feature, with an extremely low-resistivity center (< 1 Ω-m). Since a consistent high resistivity zone exists beneath each fault and its top depth is less than 5 km, so faults might not extend downward below 5 km. Earthquakes with magnitudes (ML) of 3.0 or higher predominantly occur close to the faults, when considering industrial production data, we found a noteworthy correlation between earthquakes with ML < 3.0 and annual shale gas production within the WSGB. Tectonic faulting is not the leading cause for ML < 3.0 earthquakes but likely the primary contributor to seismic events with ML ≥ 3.0.
{"title":"Crustal electrical structure and seismicity of the Weiyuan shale gas block in Sichuan basin, southwest China","authors":"Yingxing Guo, Tao Zhu, Xingbing Xie, Lei Zhou","doi":"10.1093/jge/gxad100","DOIUrl":"https://doi.org/10.1093/jge/gxad100","url":null,"abstract":"Hydraulic fracturing, a significant contributor to seismic activity within and around operational fields, has been extensively employed in shale gas production. Magnetotelluric Sounding (MT) as an effective geophysical tool for identifying high-conductivity fluid-filled and/or molten regions. In this study, we deploy a dense grid of rectangular MT sites to investigate the three-dimensional (3-D) geoelectrical resistivity structure beneath the Weiyuan shale gas block (WSGB) and subsequently examine the causes of seismic activity. The resistivity data, obtained through 3-D inversion accounting for topography using ModEM, reveals a shallow low-resistivity layer (< 10 Ω-m) within the WSGB, ranging from approximately 2 to 5 km in depth. This layer exhibits multiple isolated areas with very low resistivity (< 5 Ω-m), indicative of fluid-filled zones associated with hydraulic fracturing or shale gas-bearing formations. In the northwestern WSGB, the Weiyuan anticline presents a high-resistivity dome extending possibly to depths beyond 20 km, without extending beyond the northern boundary of our study area. Conversely, the sedimentary zone in the southeastern WSGB displays a low-resistivity feature, with an extremely low-resistivity center (< 1 Ω-m). Since a consistent high resistivity zone exists beneath each fault and its top depth is less than 5 km, so faults might not extend downward below 5 km. Earthquakes with magnitudes (ML) of 3.0 or higher predominantly occur close to the faults, when considering industrial production data, we found a noteworthy correlation between earthquakes with ML < 3.0 and annual shale gas production within the WSGB. Tectonic faulting is not the leading cause for ML < 3.0 earthquakes but likely the primary contributor to seismic events with ML ≥ 3.0.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139246687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hailong Jin, Lin Huang, Chunlai Wang, Changfeng Li, Haer Yizi, Zhian Bai, Liang Sun, Ze Zhao, Biao Chen, Yanjiang Liu
Due to the deep concave mining in Bayan Obo stope, the slope angle is steep, the terrain is high, the outcrop width of the crushing belt is large, the stability of many slopes is poor, and there are potential sliding surfaces. In this paper, through on-site investigation and sampling, the main factors affecting the landslide of the high and steep slopes of Bayan Obo are analysed. Uniaxial compression tests were carried out to obtain the mechanical parameters of dolomite and slate. With the help of the three-dimensional digital speckle system, the whole process of slope landslide under rainfall conditions was studied through similar simulation and numerical simulation experiments. The influence of rainfall on the slope of Bayan Obo and the induced pattern of landslide were revealed. The experimental results show that rainfall is the key to inducing instability, the slippage at the edge of the slope is obvious, and there is seepage in the depth, but the effect is not significant. The landslide can be roughly divided into the damage accumulation stage; the deformation development and expansion stage and the unstable slip stage.
{"title":"Induced pattern of high and steep slope landslide under rainfall conditions","authors":"Hailong Jin, Lin Huang, Chunlai Wang, Changfeng Li, Haer Yizi, Zhian Bai, Liang Sun, Ze Zhao, Biao Chen, Yanjiang Liu","doi":"10.1093/jge/gxad098","DOIUrl":"https://doi.org/10.1093/jge/gxad098","url":null,"abstract":"Due to the deep concave mining in Bayan Obo stope, the slope angle is steep, the terrain is high, the outcrop width of the crushing belt is large, the stability of many slopes is poor, and there are potential sliding surfaces. In this paper, through on-site investigation and sampling, the main factors affecting the landslide of the high and steep slopes of Bayan Obo are analysed. Uniaxial compression tests were carried out to obtain the mechanical parameters of dolomite and slate. With the help of the three-dimensional digital speckle system, the whole process of slope landslide under rainfall conditions was studied through similar simulation and numerical simulation experiments. The influence of rainfall on the slope of Bayan Obo and the induced pattern of landslide were revealed. The experimental results show that rainfall is the key to inducing instability, the slippage at the edge of the slope is obvious, and there is seepage in the depth, but the effect is not significant. The landslide can be roughly divided into the damage accumulation stage; the deformation development and expansion stage and the unstable slip stage.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139252827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fansheng Xiong, Bochen Wang, Jiawei Liu, Zhenwei Guo, Jianxin Liu
Characterizing seismic wave propagation in fluid-saturated porous media well enhances the precision of interpreting seismic data, bringing benefits to understanding reservoir properties better. Some important indicators, including wave dispersion and attenuation, along with wavefield, are widely used for interpreting the reservoir, and they can be obtained from a rock physics model. In existing models, some of them are limited in scope due to their complexity, for example, numerical solutions are difficult or costly. In view of this, this study proposes an approach of establishing equivalent dynamic equations of existing models. First, the framework of the equivalent model is derived based on Biot's theory, while the elastic coefficients are set as unknown factors. The next step is to use deep neural networks (DNNs) to predict these coefficients, and surrogate models of unknowns are established after training DNNs. The training data is naturally generated from the original model. The simplicity of the equations form, compared to the original complex model and some other equivalent manners such as viscoelastic model, enables the framework to perform wavefield simulation easier. Numerical examples show that the established equivalent model can not only predict similar dispersion and attenuation, but also obtain wavefields with small differences. This also indicates that it may be sufficient to establish an equivalent model only according to dispersion and attenuation, and the cost of generating such data is very small compared to simulating the wavefield. Therefore, the proposed approach is expected to effectively improve the computational difficulty of some existing models.
{"title":"Biot's theory-based dynamic equations modeling using machine learning auxiliary approach","authors":"Fansheng Xiong, Bochen Wang, Jiawei Liu, Zhenwei Guo, Jianxin Liu","doi":"10.1093/jge/gxad096","DOIUrl":"https://doi.org/10.1093/jge/gxad096","url":null,"abstract":"Characterizing seismic wave propagation in fluid-saturated porous media well enhances the precision of interpreting seismic data, bringing benefits to understanding reservoir properties better. Some important indicators, including wave dispersion and attenuation, along with wavefield, are widely used for interpreting the reservoir, and they can be obtained from a rock physics model. In existing models, some of them are limited in scope due to their complexity, for example, numerical solutions are difficult or costly. In view of this, this study proposes an approach of establishing equivalent dynamic equations of existing models. First, the framework of the equivalent model is derived based on Biot's theory, while the elastic coefficients are set as unknown factors. The next step is to use deep neural networks (DNNs) to predict these coefficients, and surrogate models of unknowns are established after training DNNs. The training data is naturally generated from the original model. The simplicity of the equations form, compared to the original complex model and some other equivalent manners such as viscoelastic model, enables the framework to perform wavefield simulation easier. Numerical examples show that the established equivalent model can not only predict similar dispersion and attenuation, but also obtain wavefields with small differences. This also indicates that it may be sufficient to establish an equivalent model only according to dispersion and attenuation, and the cost of generating such data is very small compared to simulating the wavefield. Therefore, the proposed approach is expected to effectively improve the computational difficulty of some existing models.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139254665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chengwei Zhang, Yunjun Zhang, Haotian Zhang, Wenpeng Bai
In this work, a new mathematical model of fractured well considering multiple factors (Permeability stress sensitivity, multiple wells interference and multiple fractures interference) is established to simulate wellbore pressure performance and rate distribution in tight gas reservoirs. The new fracture discrete coupling mathematical model is established. The wellbore pressure solution can be obtained by the pressure drop superposition and Stehfest numerical inversion. Seven flow stages are observed according to the characteristics of pressure derivative curve. The influence of several significant parameters, including rate ratio, fracture half-length, and well spacing and stress sensitivity are discussed. Based on the developed model, we demonstrated a field case to verify model accuracy. This work provides new supplementary knowledge to improve pressure data interpretation for multi-well group in tight gas reservoirs.
{"title":"Pressure and rate distribute performance of multiple fractured well with multi-wing fracture in low-permeability gas reservoirs","authors":"Chengwei Zhang, Yunjun Zhang, Haotian Zhang, Wenpeng Bai","doi":"10.1093/jge/gxad095","DOIUrl":"https://doi.org/10.1093/jge/gxad095","url":null,"abstract":"In this work, a new mathematical model of fractured well considering multiple factors (Permeability stress sensitivity, multiple wells interference and multiple fractures interference) is established to simulate wellbore pressure performance and rate distribution in tight gas reservoirs. The new fracture discrete coupling mathematical model is established. The wellbore pressure solution can be obtained by the pressure drop superposition and Stehfest numerical inversion. Seven flow stages are observed according to the characteristics of pressure derivative curve. The influence of several significant parameters, including rate ratio, fracture half-length, and well spacing and stress sensitivity are discussed. Based on the developed model, we demonstrated a field case to verify model accuracy. This work provides new supplementary knowledge to improve pressure data interpretation for multi-well group in tight gas reservoirs.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139257966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Run Jiang, Xiaodong Sun, ZhenChun Li, DongDong Peng, Liang Zhao
Seismic velocity is a critical parameter in seismic exploration, and its accuracy significantly impacts the reliability of data processing and interpretation results. However, manual velocity picking methods are not only inefficient but also time-consuming, making them increasingly inadequate for meeting the demands of practical production work. This paper introduces the Multi-Information Combination Convolutional Neural Network (MCCN) velocity auto-picking method. Building upon the foundation of convolutional neural networks, we have designed the network structure of the MCCN method specifically tailored to the characteristics of stacked velocity picking tasks. Given that velocity spectrum energy clusters exhibit both morphological and trend features, we employs a regression convolutional neural network to enhance the accuracy of velocity picking. Furthermore, as the velocity spectrum contains interference from multiple waves and other noise, we employ a coordinate attention mechanism to mitigate the influence of interfering information. Our approach involves the simultaneous incorporation of velocity spectrum and CMP information through a dual-combination network, thereby further enhancing velocity picking accuracy. Finally, we compare our method with fully connected convolutional neural networks and manual velocity picking methods, demonstrating the practicality and precision of our proposed approach.
{"title":"A multi-information combined convolutional neural network velocity spectrum automatic picking method","authors":"Run Jiang, Xiaodong Sun, ZhenChun Li, DongDong Peng, Liang Zhao","doi":"10.1093/jge/gxad090","DOIUrl":"https://doi.org/10.1093/jge/gxad090","url":null,"abstract":"Seismic velocity is a critical parameter in seismic exploration, and its accuracy significantly impacts the reliability of data processing and interpretation results. However, manual velocity picking methods are not only inefficient but also time-consuming, making them increasingly inadequate for meeting the demands of practical production work. This paper introduces the Multi-Information Combination Convolutional Neural Network (MCCN) velocity auto-picking method. Building upon the foundation of convolutional neural networks, we have designed the network structure of the MCCN method specifically tailored to the characteristics of stacked velocity picking tasks. Given that velocity spectrum energy clusters exhibit both morphological and trend features, we employs a regression convolutional neural network to enhance the accuracy of velocity picking. Furthermore, as the velocity spectrum contains interference from multiple waves and other noise, we employ a coordinate attention mechanism to mitigate the influence of interfering information. Our approach involves the simultaneous incorporation of velocity spectrum and CMP information through a dual-combination network, thereby further enhancing velocity picking accuracy. Finally, we compare our method with fully connected convolutional neural networks and manual velocity picking methods, demonstrating the practicality and precision of our proposed approach.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139273472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yaojun Wang, Jingjing Zong, Liangji Wang, Bangli Zou, Ziteng Chen, Yang Luo
Despite the extensive application of artificial neural networks in seismic inversion, their effectiveness is often hampered by the limited availability of labeled data. To address this challenge, we introduce a novel method for seismic impedance inversion. Our approach integrates a physics-driven cycle network with a Conditional Generative Adversarial Network (CGAN) and a convolutional model. Employing seismic data as input, the CGAN capitalizes on inherent information to minimize non-uniqueness during inversion. Furthermore, the convolutional model, acting as a physics-informed operator, reverts the derived impedance data back to seismic form, enabling simultaneous training of neural networks with labeled and unlabeled data, fulfilling the seismic-to-seismic cycle. The proposed method is demonstrated to be effective on tests using both theoretical models and field data.
{"title":"Physics-driven cycle network for seismic impedance inversion using conditional generative adversarial networks","authors":"Yaojun Wang, Jingjing Zong, Liangji Wang, Bangli Zou, Ziteng Chen, Yang Luo","doi":"10.1093/jge/gxad093","DOIUrl":"https://doi.org/10.1093/jge/gxad093","url":null,"abstract":"Despite the extensive application of artificial neural networks in seismic inversion, their effectiveness is often hampered by the limited availability of labeled data. To address this challenge, we introduce a novel method for seismic impedance inversion. Our approach integrates a physics-driven cycle network with a Conditional Generative Adversarial Network (CGAN) and a convolutional model. Employing seismic data as input, the CGAN capitalizes on inherent information to minimize non-uniqueness during inversion. Furthermore, the convolutional model, acting as a physics-informed operator, reverts the derived impedance data back to seismic form, enabling simultaneous training of neural networks with labeled and unlabeled data, fulfilling the seismic-to-seismic cycle. The proposed method is demonstrated to be effective on tests using both theoretical models and field data.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chengyun Song, Shutao Guo, Chuanchao Xiong, Jiying Tuo
Deep learning methods achieve excellent noise reduction performances in seismic data processing compared with traditional methods. However, deep learning usually requires a large number of pairwise noisy-clean training data, which is an extremely challenging task. In this paper, an unsupervised approach without clean seismic data is proposed to suppress random noise. Seismic data is divided into odd and even traces, which serve as the input and output of the depth network. So that the proposed algorithm can be trained directly on the original data. What is more, the proposed method introduces two regularization terms to solve the over-smoothing problem caused by reconstruction of adjacent traces. The first term considers an ideal denoising network that does not cause oversmooth as a constraint, while the second term considers the structural information existing in seismic data. Experiments on synthetic post-stack data illustrate that the proposed method obtain the higher SNR than the comparison methods. In the application of field post-stack seismic data, the proposed method can effectively maintain the seismic amplitude and generate good spectral characteristics.
{"title":"Regularized deep learning for unsupervised random noise attenuation in poststack seismic data","authors":"Chengyun Song, Shutao Guo, Chuanchao Xiong, Jiying Tuo","doi":"10.1093/jge/gxad094","DOIUrl":"https://doi.org/10.1093/jge/gxad094","url":null,"abstract":"Deep learning methods achieve excellent noise reduction performances in seismic data processing compared with traditional methods. However, deep learning usually requires a large number of pairwise noisy-clean training data, which is an extremely challenging task. In this paper, an unsupervised approach without clean seismic data is proposed to suppress random noise. Seismic data is divided into odd and even traces, which serve as the input and output of the depth network. So that the proposed algorithm can be trained directly on the original data. What is more, the proposed method introduces two regularization terms to solve the over-smoothing problem caused by reconstruction of adjacent traces. The first term considers an ideal denoising network that does not cause oversmooth as a constraint, while the second term considers the structural information existing in seismic data. Experiments on synthetic post-stack data illustrate that the proposed method obtain the higher SNR than the comparison methods. In the application of field post-stack seismic data, the proposed method can effectively maintain the seismic amplitude and generate good spectral characteristics.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139277878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Min Yang, Xinqiang Xu, Wanyin Wang, Dongming Zhao, Wei Zhou
Abstract Mapping the density contrast through the 3D gravity inversion can help detect the goals under the subsurface. However, it is a challenge to accurately and efficiently solve the 3D gravity inversion. Krylov subspace method is commonly used for large linear problems due to its high computational efficiency and low storage requirement. In this study, two classical algorithms of Krylov subspace method, namely the Generalized Minimum Residual method and the Conjugate Gradient method, are applied to 3D gravity inversion. Based on the recovered models of the deep mineral and the shallow L-shaped tunnel models, it was found that the Generalized Minimum Residual method provided similar density contrast results as the Conjugate Gradient method. The obtained inversion results of density contrast corresponded well to the position of the deep mineral resources model and the L-shaped tunnel model. The 3D distribution of Fe content underground was obtained by inverting the measured gravity data from Olympic Dam in Australia. The recovered results correspond well with the distribution of Fe content in the geological profile collected. The accuracy of inversion using the Generalized Minimum Residual method was similar to that of the Conjugate Gradient method under the same conditions. However, the Generalized Minimum Residual method had a faster convergence speed and increased inversion efficiency by about 90%, greatly reducing the inversion time and improves the inversion efficiency.
{"title":"3D Gravity Fast Inversion Based on Krylov Subspace Methods","authors":"Min Yang, Xinqiang Xu, Wanyin Wang, Dongming Zhao, Wei Zhou","doi":"10.1093/jge/gxad091","DOIUrl":"https://doi.org/10.1093/jge/gxad091","url":null,"abstract":"Abstract Mapping the density contrast through the 3D gravity inversion can help detect the goals under the subsurface. However, it is a challenge to accurately and efficiently solve the 3D gravity inversion. Krylov subspace method is commonly used for large linear problems due to its high computational efficiency and low storage requirement. In this study, two classical algorithms of Krylov subspace method, namely the Generalized Minimum Residual method and the Conjugate Gradient method, are applied to 3D gravity inversion. Based on the recovered models of the deep mineral and the shallow L-shaped tunnel models, it was found that the Generalized Minimum Residual method provided similar density contrast results as the Conjugate Gradient method. The obtained inversion results of density contrast corresponded well to the position of the deep mineral resources model and the L-shaped tunnel model. The 3D distribution of Fe content underground was obtained by inverting the measured gravity data from Olympic Dam in Australia. The recovered results correspond well with the distribution of Fe content in the geological profile collected. The accuracy of inversion using the Generalized Minimum Residual method was similar to that of the Conjugate Gradient method under the same conditions. However, the Generalized Minimum Residual method had a faster convergence speed and increased inversion efficiency by about 90%, greatly reducing the inversion time and improves the inversion efficiency.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Digital rock characterization enables high-fidelity quantification of core samples, facilitating computational studies of physical properties at the microscopic scale. Multiscale tomographic imaging resolves microstructural features from sub-nanometer to millimeter dimensions. However, single-resolution volumes preclude capturing cross-scale morphological attributes due to the inverse relationship between the field of view and resolution. Constructing multiscale, multiresolution, multiphase digital rock model is therefore imperative for reconciling this paradox. We performed multiscale scanning imaging on tight sandstone samples. Based on pore network model integration algorithms, we constructed dual-scale pore network model (PNM) and fracture-pore hybrid network model to analyze their flow characteristics. Results showed that the absolute permeability of the dual-scale PNM exhibited a distinct linear increase with the number of extra cross-scale throats and throat factor, but the rate of increase became smaller when the throat factor exceeded 0.6. For dual-scale pore network with cross-scale throat and throat factor of 1 and 0.7, the predicted porosity matched experimental results well. For the fracture-pore hybrid network model, the relationship between absolute permeability and cross-scale throat properties is similar to the dual-scale PNM. When fluid flow was parallel to the fracture orientation, permeability increased markedly with fracture aperture as a power law function. However, the dip angle did not induce obvious permeability variation trends across different flow directions.
{"title":"Multiscale Pore Network Modeling and Flow Property Analysis for Tight Sandstone: A case study","authors":"Xiang Wu, Fei Wang, Zhanshan Xiao, Yonghao Zhang, Jianbin Zhao, Chaoqiang Fang, Bo Wei","doi":"10.1093/jge/gxad092","DOIUrl":"https://doi.org/10.1093/jge/gxad092","url":null,"abstract":"Abstract Digital rock characterization enables high-fidelity quantification of core samples, facilitating computational studies of physical properties at the microscopic scale. Multiscale tomographic imaging resolves microstructural features from sub-nanometer to millimeter dimensions. However, single-resolution volumes preclude capturing cross-scale morphological attributes due to the inverse relationship between the field of view and resolution. Constructing multiscale, multiresolution, multiphase digital rock model is therefore imperative for reconciling this paradox. We performed multiscale scanning imaging on tight sandstone samples. Based on pore network model integration algorithms, we constructed dual-scale pore network model (PNM) and fracture-pore hybrid network model to analyze their flow characteristics. Results showed that the absolute permeability of the dual-scale PNM exhibited a distinct linear increase with the number of extra cross-scale throats and throat factor, but the rate of increase became smaller when the throat factor exceeded 0.6. For dual-scale pore network with cross-scale throat and throat factor of 1 and 0.7, the predicted porosity matched experimental results well. For the fracture-pore hybrid network model, the relationship between absolute permeability and cross-scale throat properties is similar to the dual-scale PNM. When fluid flow was parallel to the fracture orientation, permeability increased markedly with fracture aperture as a power law function. However, the dip angle did not induce obvious permeability variation trends across different flow directions.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Understanding the influence of geological characteristics on rock physics properties is crucial for accurately recognizing the relationship between rock physics variation and reservoir characteristics. Unlike the conventional rock species, the rock physics properties of the deep carbonate rocks in the third member of Yingshan Formation (Ying-III Member) in Gucheng area, Tarim Basin are relatively more complex. To address this problem, we investigated the rock physics characteristics and controlling factors of different sedimentary microfacies samples, combined with sedimentological analysis and rock physics experiments. The results show that the sedimentary environment affects the lithology and pore structure by controlling the properties of the primitive rock and early diagenesis. Dolomitized shoal microfacies and shoal top dolomitic flat microfacies primarily form crystalline dolomite and siliceous dolomite, with pores consisting of inter-crystalline pores, dissolution pores, and cracks. Inter-shoal dolomitic flat microfacies develops silty dolomite, with only a few inter-crystalline pores and cracks. Middle-high energy shoal microfacies and inter-shoal sea microfacies develop tight calcarenite and micritic limestone. Samples with similar mineral composition have relatively consistent density values and acoustic properties. Soft pores, such as micro cracks, have a significant impact on the effective pressure and acoustic wave velocity, velocity and velocity ratio, and velocity and porosity relationships. The research can show a new approach for the rock physics characteristics of deep carbonate reservoirs under geological background constraints, as well as the rock physics basis for seismic prediction of Ying-III Member reservoir.
{"title":"Rock physics characteristics and their control factors of carbonate in different sedimentary microfacies of the Yingshan Formation, Gucheng Area, Tarim Basin","authors":"Jiaqing Wang, Jixin Deng, Hui Xia, Longlong Yan","doi":"10.1093/jge/gxad087","DOIUrl":"https://doi.org/10.1093/jge/gxad087","url":null,"abstract":"Abstract Understanding the influence of geological characteristics on rock physics properties is crucial for accurately recognizing the relationship between rock physics variation and reservoir characteristics. Unlike the conventional rock species, the rock physics properties of the deep carbonate rocks in the third member of Yingshan Formation (Ying-III Member) in Gucheng area, Tarim Basin are relatively more complex. To address this problem, we investigated the rock physics characteristics and controlling factors of different sedimentary microfacies samples, combined with sedimentological analysis and rock physics experiments. The results show that the sedimentary environment affects the lithology and pore structure by controlling the properties of the primitive rock and early diagenesis. Dolomitized shoal microfacies and shoal top dolomitic flat microfacies primarily form crystalline dolomite and siliceous dolomite, with pores consisting of inter-crystalline pores, dissolution pores, and cracks. Inter-shoal dolomitic flat microfacies develops silty dolomite, with only a few inter-crystalline pores and cracks. Middle-high energy shoal microfacies and inter-shoal sea microfacies develop tight calcarenite and micritic limestone. Samples with similar mineral composition have relatively consistent density values and acoustic properties. Soft pores, such as micro cracks, have a significant impact on the effective pressure and acoustic wave velocity, velocity and velocity ratio, and velocity and porosity relationships. The research can show a new approach for the rock physics characteristics of deep carbonate reservoirs under geological background constraints, as well as the rock physics basis for seismic prediction of Ying-III Member reservoir.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135429901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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