Abstract Rocks and other geological materials have appreciable dispersion in their elastic properties. Rock elastic parameters within the same frequency range as the logging frequency band (1–20 kHz) should be determined to facilitate reservoir prediction and interpretation of logging data. This study suggests a technique for determining the elastic characteristics of rock cores at low frequencies using a sandwich resonant bar by integrating transfer matrices into the one-dimensional transmission model. The frequency response expression of the sandwich resonant bar is derived analytically and then the response is simulated accurately based on this expression. Numerical results show that the first two-order longitudinal resonance frequencies are approximately linearly related to the inverse of the sample's Young's modulus and the density, respectively. In addition, an inversion algorithm based on Gauss–Newton iteration, which converges faster and more efficiently, is proposed in this paper. The residuals between the model's first two resonant frequencies and the simulated results are used as the error function, and the elasticity parameters that minimize the error function are the best estimate for creating the model. This research is valuable for measuring rock elastic parameters accurately in the kilohertz range, which is of practical significance in dispersion-related studies relating to rock cores.
{"title":"A sandwich resonant bar method with transfer matrices for measuring the elastic parameters of rock at low frequency","authors":"Jiahui Li, Dehua Chen, Yu Wang, Hao Chen","doi":"10.1093/jge/gxad078","DOIUrl":"https://doi.org/10.1093/jge/gxad078","url":null,"abstract":"Abstract Rocks and other geological materials have appreciable dispersion in their elastic properties. Rock elastic parameters within the same frequency range as the logging frequency band (1–20 kHz) should be determined to facilitate reservoir prediction and interpretation of logging data. This study suggests a technique for determining the elastic characteristics of rock cores at low frequencies using a sandwich resonant bar by integrating transfer matrices into the one-dimensional transmission model. The frequency response expression of the sandwich resonant bar is derived analytically and then the response is simulated accurately based on this expression. Numerical results show that the first two-order longitudinal resonance frequencies are approximately linearly related to the inverse of the sample's Young's modulus and the density, respectively. In addition, an inversion algorithm based on Gauss–Newton iteration, which converges faster and more efficiently, is proposed in this paper. The residuals between the model's first two resonant frequencies and the simulated results are used as the error function, and the elasticity parameters that minimize the error function are the best estimate for creating the model. This research is valuable for measuring rock elastic parameters accurately in the kilohertz range, which is of practical significance in dispersion-related studies relating to rock cores.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135579135","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 High-quality seismic data contribute to improving the prediction accuracy of complex reservoirs. Currently, there are several methods for enhancing pre-stack gather resolution and signal-to-noise ratio (SNR). Meanwhile, to avoid incorrect seismic interpretation, we must evaluate whether the processed data maintains its original characteristics and reliability. This paper establishes an integrated workflow for optimizing common-reflection-point (CRP) gather that is divided into processing and quality control. Furthermore, we summarize the comprehensive quality control means and explain their specific significance in each step. Data processing includes four parts: gather flattening, SNR enhancement, energy compensation, and resolution improvement. Simultaneously, we use well log data, forward simulation, stack data, and inversion to guarantee the processed data is optimized and keeps its original characteristics. Applications in carbonate reservoir CRP gathers demonstrate that this workflow can comprehensively optimize pre-stack seismic data and improve SNR and resolution. Importantly, the quality controls guarantee results have improved accuracy in reservoir prediction and stronger correlation coefficients with well data.
{"title":"An Integrated Workflow for Pre-stack Gather Optimization","authors":"Jie Zhou, Huailai Zhou, Junping Liu, Yaoguang Sun","doi":"10.1093/jge/gxad077","DOIUrl":"https://doi.org/10.1093/jge/gxad077","url":null,"abstract":"Abstract High-quality seismic data contribute to improving the prediction accuracy of complex reservoirs. Currently, there are several methods for enhancing pre-stack gather resolution and signal-to-noise ratio (SNR). Meanwhile, to avoid incorrect seismic interpretation, we must evaluate whether the processed data maintains its original characteristics and reliability. This paper establishes an integrated workflow for optimizing common-reflection-point (CRP) gather that is divided into processing and quality control. Furthermore, we summarize the comprehensive quality control means and explain their specific significance in each step. Data processing includes four parts: gather flattening, SNR enhancement, energy compensation, and resolution improvement. Simultaneously, we use well log data, forward simulation, stack data, and inversion to guarantee the processed data is optimized and keeps its original characteristics. Applications in carbonate reservoir CRP gathers demonstrate that this workflow can comprehensively optimize pre-stack seismic data and improve SNR and resolution. Importantly, the quality controls guarantee results have improved accuracy in reservoir prediction and stronger correlation coefficients with well data.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135966591","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 In this paper, a method for seismic random noise detection and suppression using a denoising convolutional neural network (DnCNN) is presented. Thanks to the residual learning and batch normalization, deep learning networks can converge faster, the gradient descent and disappearance due to the increase of network layers are solved, and the residual results can be predicted more accurately. For seismic data, the variance estimation method is useful for obtaining an accurate noise distribution model and statistical parameters that provide a useful assessment of the noise level. With the variance estimation method based on weak texture blocks, a noise distribution model and statistical parameters can be derived with high accuracy, and this method effectively estimates seismic noise levels. The DnCNN networks are trained, and non-Gaussian noise reduction technology is used to achieve blind noise reduction at unknown levels, improving the noise reduction of seismic data. In addition, stratigraphic dip characteristics related to layer structure are used as DnCNN training network constraints to prevent effective signals in seismic data from being corrupted by conventional DnCNN noise reduction methods. Geological features such as faults and fracture-cavities can be effectively protected. Carbonate faults in the Tarim Basin in China are affected by the desert surface and the depth at which reservoirs are buried. The seismic data has a low signal-to-noise ratio, and the effective signals of the reservoir are low resolution. The seismic data can be effectively enhanced with this method for noise reduction in this area, the fracture-cavity is effectively displayed, and the fault features are also highlighted.
{"title":"Seismic random noise attenuation using DnCNN with stratigraphic dip constraint","authors":"Wei Yang, Xuehua Chen, Ying Rao","doi":"10.1093/jge/gxad076","DOIUrl":"https://doi.org/10.1093/jge/gxad076","url":null,"abstract":"Abstract In this paper, a method for seismic random noise detection and suppression using a denoising convolutional neural network (DnCNN) is presented. Thanks to the residual learning and batch normalization, deep learning networks can converge faster, the gradient descent and disappearance due to the increase of network layers are solved, and the residual results can be predicted more accurately. For seismic data, the variance estimation method is useful for obtaining an accurate noise distribution model and statistical parameters that provide a useful assessment of the noise level. With the variance estimation method based on weak texture blocks, a noise distribution model and statistical parameters can be derived with high accuracy, and this method effectively estimates seismic noise levels. The DnCNN networks are trained, and non-Gaussian noise reduction technology is used to achieve blind noise reduction at unknown levels, improving the noise reduction of seismic data. In addition, stratigraphic dip characteristics related to layer structure are used as DnCNN training network constraints to prevent effective signals in seismic data from being corrupted by conventional DnCNN noise reduction methods. Geological features such as faults and fracture-cavities can be effectively protected. Carbonate faults in the Tarim Basin in China are affected by the desert surface and the depth at which reservoirs are buried. The seismic data has a low signal-to-noise ratio, and the effective signals of the reservoir are low resolution. The seismic data can be effectively enhanced with this method for noise reduction in this area, the fracture-cavity is effectively displayed, and the fault features are also highlighted.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061785","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 Shale gas has become a major source of natural gas production and has received worldwide attention. Hydraulic fracturing is widely performed to stimulate oil and gas wells with considerable success. Given high-precision microseismic (MS) event locations, we can predict the development trend and region of fracturing and evaluate the stimulation effect, thereby providing technical guidance for subsequent exploitation. An accurate velocity model is essential for MS event positioning. However, simple velocity models, such as the uniform or vertical transverse isotropy (VTI) velocity model, are generally applied to calibrate the velocity model. Despite calibration, the VTI model may still face challenges in obtaining accurate MS event locations. Based on the structural characteristics of shale, we propose a novel local velocity model calibration algorithm for surface MS monitoring. To calibrate the velocity model, the actual strata interfaces are replaced with 3D gently inclined planes. We use very fast simulated annealing to concurrently tune the velocity, depth, and angle parameters of the model. Through the assessment of both the stacked amplitude at the position of the perforation shot and the relocation error of the perforation shot, we determine the ideal velocity model. To evaluate the effectiveness of our approach, we conduct experiments on both a synthetic model and a field dataset, and statistically analyze the location error. The results show that the proposed method obviously reduces the perforation shot relocation error and is well-suited for calibrating velocity models that are close to slightly inhomogeneous layered media.
{"title":"Velocity model calibration for surface microseismic monitoring based on a 3D gently inclined layered equivalent model","authors":"Chunlu Wang, Yanfei Wei, Feng Sun, Xiaohua Zhou, Haiyu Jiang, Zubin Chen","doi":"10.1093/jge/gxad071","DOIUrl":"https://doi.org/10.1093/jge/gxad071","url":null,"abstract":"Abstract Shale gas has become a major source of natural gas production and has received worldwide attention. Hydraulic fracturing is widely performed to stimulate oil and gas wells with considerable success. Given high-precision microseismic (MS) event locations, we can predict the development trend and region of fracturing and evaluate the stimulation effect, thereby providing technical guidance for subsequent exploitation. An accurate velocity model is essential for MS event positioning. However, simple velocity models, such as the uniform or vertical transverse isotropy (VTI) velocity model, are generally applied to calibrate the velocity model. Despite calibration, the VTI model may still face challenges in obtaining accurate MS event locations. Based on the structural characteristics of shale, we propose a novel local velocity model calibration algorithm for surface MS monitoring. To calibrate the velocity model, the actual strata interfaces are replaced with 3D gently inclined planes. We use very fast simulated annealing to concurrently tune the velocity, depth, and angle parameters of the model. Through the assessment of both the stacked amplitude at the position of the perforation shot and the relocation error of the perforation shot, we determine the ideal velocity model. To evaluate the effectiveness of our approach, we conduct experiments on both a synthetic model and a field dataset, and statistically analyze the location error. The results show that the proposed method obviously reduces the perforation shot relocation error and is well-suited for calibrating velocity models that are close to slightly inhomogeneous layered media.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136061444","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 The finite difference forward modeling has been widely used in geophysics exploration and petroleum fields. Because of its high efficiency and easy application for graphical processing units, it has been widely concerned by industry and academia. However, owing to many factors, the problem of numerical dispersion has been an important factor hindering this method. To overcome the numerical dispersion, this paper proposes a method for removing numerical dispersion using deep learning. Unlike the conventional optimized algorithms target to optimize the finite difference coefficients, our strategy is based on big data training to eliminate the dispersion data after seismic data modeling. We design a neural network architecture based on cycle-consistent generative adversarial networks (Cycle-GANs) and residual learning for elastic wave propagation. Under the premise of not significantly increasing the calculation time, we can obtain higher calculation accuracy. Compared with the high-order finite difference algorithm, the calculation time is the advantage of our proposed deep learning method. Tests prove the efficiency and stability of our proposed algorithm.
{"title":"A new approach to remove the numerical dispersion in elastic wave modeling using R-Cycle-GAN networks","authors":"Wanqiu Zheng, Jian Wang, Xiaohong Meng","doi":"10.1093/jge/gxad074","DOIUrl":"https://doi.org/10.1093/jge/gxad074","url":null,"abstract":"Abstract The finite difference forward modeling has been widely used in geophysics exploration and petroleum fields. Because of its high efficiency and easy application for graphical processing units, it has been widely concerned by industry and academia. However, owing to many factors, the problem of numerical dispersion has been an important factor hindering this method. To overcome the numerical dispersion, this paper proposes a method for removing numerical dispersion using deep learning. Unlike the conventional optimized algorithms target to optimize the finite difference coefficients, our strategy is based on big data training to eliminate the dispersion data after seismic data modeling. We design a neural network architecture based on cycle-consistent generative adversarial networks (Cycle-GANs) and residual learning for elastic wave propagation. Under the premise of not significantly increasing the calculation time, we can obtain higher calculation accuracy. Compared with the high-order finite difference algorithm, the calculation time is the advantage of our proposed deep learning method. Tests prove the efficiency and stability of our proposed algorithm.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135690085","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 Nonlinear inverse problems arise in various fields ranging from scientific computation to engineering technology. Inverse problems are intrinsically ill-posed, and effective regularization techniques are necessary. The core of a suitable regularization method is to introduce the prior information of the model via an explicit or implicit regularization function. Plug-and-play regularization is a flexible framework that integrates the most effective denoising priors into an iterative algorithm, and it has recently shown great potential in the solution of linear ill-posed problems. Unlike traditional regularization methods, plug-and-play regularization does not require an explicit regularization function to represent the prior information of the model. In this work, by using total variation, block-matching and three-dimensional filtering, and fast and flexible denoising convolutional neural network denoisers, we propose a novel iterative regularization algorithm based on the alternating direction method of multipliers method. The combination of total variation and block-matching three-dimensional filtering regularizers can take advantage of the sparsity and nonlocal similarity in the solution of inverse problems. When combined with traditional and novel regularizers, deep neural networks have been shown to be an effective regularization approach, which can achieve state-of-the-art performance. Finally, we apply the proposed algorithm to the full waveform inversion problem to show the effectiveness of our method. Numerical results demonstrate that the proposed algorithm outperforms existing inversion methods in terms of quantitative measures and visual perceptual quality.
{"title":"An efficient plug-and-play regularization method for full waveform inversion","authors":"Hongsun Fu, Lu Yang, Xinyue Miao","doi":"10.1093/jge/gxad073","DOIUrl":"https://doi.org/10.1093/jge/gxad073","url":null,"abstract":"Abstract Nonlinear inverse problems arise in various fields ranging from scientific computation to engineering technology. Inverse problems are intrinsically ill-posed, and effective regularization techniques are necessary. The core of a suitable regularization method is to introduce the prior information of the model via an explicit or implicit regularization function. Plug-and-play regularization is a flexible framework that integrates the most effective denoising priors into an iterative algorithm, and it has recently shown great potential in the solution of linear ill-posed problems. Unlike traditional regularization methods, plug-and-play regularization does not require an explicit regularization function to represent the prior information of the model. In this work, by using total variation, block-matching and three-dimensional filtering, and fast and flexible denoising convolutional neural network denoisers, we propose a novel iterative regularization algorithm based on the alternating direction method of multipliers method. The combination of total variation and block-matching three-dimensional filtering regularizers can take advantage of the sparsity and nonlocal similarity in the solution of inverse problems. When combined with traditional and novel regularizers, deep neural networks have been shown to be an effective regularization approach, which can achieve state-of-the-art performance. Finally, we apply the proposed algorithm to the full waveform inversion problem to show the effectiveness of our method. Numerical results demonstrate that the proposed algorithm outperforms existing inversion methods in terms of quantitative measures and visual perceptual quality.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135878332","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}
{"title":"Correction to: Augmented deep learning workflow for robust fault prediction over multiple tectonic regimes","authors":"","doi":"10.1093/jge/gxad075","DOIUrl":"https://doi.org/10.1093/jge/gxad075","url":null,"abstract":"","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135935724","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}
Guanghui Hu, Wencai Xu, Binghong He, Weiguang He, Zeyuan Du, Jiashun Yao
Abstract The exploration zone in Northwest China poses unique challenges due to its complex structures, fractured blocks, deep reservoirs, and geological anomalies. Conventional velocity modeling and migration imaging methods face difficulties in accurately imaging the underlying reservoirs beneath abnormal rock formations, resulting in poor signal-to-noise ratio and resolution. Moreover, the seismic data quality often falls short, leading to unfocused images. To address these challenges, we propose a novel approach: dynamic image warping-based reflection full waveform inversion. This method combines a model regularization strategy and structural constraints to generate high-resolution velocity models. The application in the Taha Industrial Zone demonstrates that the proposed method effectively characterizes the velocity characteristics of special geological bodies, such as deep weathering crusts, improving imaging effects and resolution, which allows for a more precise understanding of the complex geological features.
{"title":"DIW based reflection full waveform inversion and its application of land data","authors":"Guanghui Hu, Wencai Xu, Binghong He, Weiguang He, Zeyuan Du, Jiashun Yao","doi":"10.1093/jge/gxad070","DOIUrl":"https://doi.org/10.1093/jge/gxad070","url":null,"abstract":"Abstract The exploration zone in Northwest China poses unique challenges due to its complex structures, fractured blocks, deep reservoirs, and geological anomalies. Conventional velocity modeling and migration imaging methods face difficulties in accurately imaging the underlying reservoirs beneath abnormal rock formations, resulting in poor signal-to-noise ratio and resolution. Moreover, the seismic data quality often falls short, leading to unfocused images. To address these challenges, we propose a novel approach: dynamic image warping-based reflection full waveform inversion. This method combines a model regularization strategy and structural constraints to generate high-resolution velocity models. The application in the Taha Industrial Zone demonstrates that the proposed method effectively characterizes the velocity characteristics of special geological bodies, such as deep weathering crusts, improving imaging effects and resolution, which allows for a more precise understanding of the complex geological features.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135048004","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 Faults encountered during coal mining can compromise the continuity and integrity of the overburden, resulting in considerable differences in the stress, displacement, and failure fields of the rocks surrounding the fault zone. When a working face is located adjacent to a fault, the fault-disturbed overburden becomes activated and unstable along the fault plane, which could lead to mining disasters. The fault-adjacent overburden morphology during mining was analyzed using a physical model. A mechanical model of the stability of the fault-disturbed overburden was constructed. The criteria for determining the sliding failure of the overburden during mining were defined, from which the critical coal pillar width required to maintain the overburden stability was determined. The results indicate that an inverted trapezoidal block forms in the overburden due to the combined effects of mining and faulting. The morphology of this block is influenced by the coal pillar width, the height of the fractured zone, and the dip angles of fault and coal seam. The block is prone to sliding or rotational failure along the fault plane during mining. As the coal seam and fault dip angles increase, the critical coal pillar width for maintaining overburden stability decreases. Conversely, increasing coal seam thickness increases the critical coal pillar width. The critical width of coal pillar was determined to be 176 m, which was verified through field observations performed in the #3307 working face.
{"title":"Failure analysis of overlying strata in fault fracture zone during coal mining","authors":"Feng Wang, Tong Chen, Zetao Chen, Shaojie Chen, Xiyang Ding, Zunxin Liu","doi":"10.1093/jge/gxad072","DOIUrl":"https://doi.org/10.1093/jge/gxad072","url":null,"abstract":"Abstract Faults encountered during coal mining can compromise the continuity and integrity of the overburden, resulting in considerable differences in the stress, displacement, and failure fields of the rocks surrounding the fault zone. When a working face is located adjacent to a fault, the fault-disturbed overburden becomes activated and unstable along the fault plane, which could lead to mining disasters. The fault-adjacent overburden morphology during mining was analyzed using a physical model. A mechanical model of the stability of the fault-disturbed overburden was constructed. The criteria for determining the sliding failure of the overburden during mining were defined, from which the critical coal pillar width required to maintain the overburden stability was determined. The results indicate that an inverted trapezoidal block forms in the overburden due to the combined effects of mining and faulting. The morphology of this block is influenced by the coal pillar width, the height of the fractured zone, and the dip angles of fault and coal seam. The block is prone to sliding or rotational failure along the fault plane during mining. As the coal seam and fault dip angles increase, the critical coal pillar width for maintaining overburden stability decreases. Conversely, increasing coal seam thickness increases the critical coal pillar width. The critical width of coal pillar was determined to be 176 m, which was verified through field observations performed in the #3307 working face.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135047682","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 Tight reservoirs have poor physical properties and complex pore structures, and well production is often affected by the starting threshold gradient, stress sensitivity, and water blockage. In this paper, the numerical simulation method is used to make these three factors equal. In addition, normalized influence coefficient analysis and grey relation analysis are used to investigate the degree of influence of each factor on well production. In this study, three methods are developed to set the threshold pressure gradient according to the permeability zoning, and the effect of reservoir heterogeneity is considered to set the threshold pressure gradient for unconventional reservoirs. The equivalent accuracy of the numerical simulation of the threshold pressure gradient can be improved compared to the traditional method. Stress sensitivity and water blockage effects are equated by correcting for rock compressibility coefficient and gas relative permeability. The fit rate of the gas well production history is improved by 2–3% after considering complex factors. The inclusion of the complex factors reduces the reservoir energy mobilization. The threshold pressure gradient results in an additional pressure reduction of about 1.8 MPa around the gas well. Residual gas identification and development is helped by clarifying the effect of complex factors on formation pressure When only the effect of a single factor is considered, water blockage has the most significant effect on gas well production, followed by threshold pressure and the weakest stress sensitivity. When several factors are considered together, the effect of stress sensitivity is increased.
{"title":"Flow parameter setting methods in numerical simulation of the unconventional reservoir and its impact on production","authors":"Bince Li, Fengpeng Lai, Guanglei Ren, Huachao Sun","doi":"10.1093/jge/gxad068","DOIUrl":"https://doi.org/10.1093/jge/gxad068","url":null,"abstract":"Abstract Tight reservoirs have poor physical properties and complex pore structures, and well production is often affected by the starting threshold gradient, stress sensitivity, and water blockage. In this paper, the numerical simulation method is used to make these three factors equal. In addition, normalized influence coefficient analysis and grey relation analysis are used to investigate the degree of influence of each factor on well production. In this study, three methods are developed to set the threshold pressure gradient according to the permeability zoning, and the effect of reservoir heterogeneity is considered to set the threshold pressure gradient for unconventional reservoirs. The equivalent accuracy of the numerical simulation of the threshold pressure gradient can be improved compared to the traditional method. Stress sensitivity and water blockage effects are equated by correcting for rock compressibility coefficient and gas relative permeability. The fit rate of the gas well production history is improved by 2–3% after considering complex factors. The inclusion of the complex factors reduces the reservoir energy mobilization. The threshold pressure gradient results in an additional pressure reduction of about 1.8 MPa around the gas well. Residual gas identification and development is helped by clarifying the effect of complex factors on formation pressure When only the effect of a single factor is considered, water blockage has the most significant effect on gas well production, followed by threshold pressure and the weakest stress sensitivity. When several factors are considered together, the effect of stress sensitivity is increased.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135047674","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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