Journal of Geophysics and Engineering最新文献_第2页

Biot's theory-based dynamic equations modeling using machine learning auxiliary approach 利用机器学习辅助方法建立基于毕奥理论的动态方程模型

IF 1.4 3区地球科学

Journal of Geophysics and Engineering Pub Date : 2023-11-20 DOI: 10.1093/jge/gxad096

Fansheng Xiong, Bochen Wang, Jiawei Liu, Zhenwei Guo, Jianxin Liu

{"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":"53 5","pages":""},"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}

引用次数: 0

Pressure and rate distribute performance of multiple fractured well with multi-wing fracture in low-permeability gas reservoirs 低渗透气藏多翼压裂井的压力和速率分布性能

IF 1.4 3区地球科学

Journal of Geophysics and Engineering Pub Date : 2023-11-20 DOI: 10.1093/jge/gxad095

Chengwei Zhang, Yunjun Zhang, Haotian Zhang, Wenpeng Bai

引用次数: 0

A multi-information combined convolutional neural network velocity spectrum automatic picking method 一种多信息组合卷积神经网络速度谱自动采摘方法

IF 1.4 3区地球科学

Journal of Geophysics and Engineering Pub Date : 2023-11-15 DOI: 10.1093/jge/gxad090

Run Jiang, Xiaodong Sun, ZhenChun Li, DongDong Peng, Liang Zhao

{"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":"2 2","pages":""},"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}

引用次数: 0

Physics-driven cycle network for seismic impedance inversion using conditional generative adversarial networks 利用条件生成对抗网络进行地震阻抗反演的物理驱动循环网络

IF 1.4 3区地球科学

Journal of Geophysics and Engineering Pub Date : 2023-11-15 DOI: 10.1093/jge/gxad093

Yaojun Wang, Jingjing Zong, Liangji Wang, Bangli Zou, Ziteng Chen, Yang Luo

引用次数: 0

Regularized deep learning for unsupervised random noise attenuation in poststack seismic data 用于叠后地震数据无监督随机噪声衰减的正则化深度学习

IF 1.4 3区地球科学

Journal of Geophysics and Engineering Pub Date : 2023-11-14 DOI: 10.1093/jge/gxad094

Chengyun Song, Shutao Guo, Chuanchao Xiong, Jiying Tuo

{"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":"47 3","pages":""},"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}

引用次数: 0

3D Gravity Fast Inversion Based on Krylov Subspace Methods 基于Krylov子空间方法的三维重力快速反演

3区地球科学

Journal of Geophysics and Engineering Pub Date : 2023-11-13 DOI: 10.1093/jge/gxad091

Min Yang, Xinqiang Xu, Wanyin Wang, Dongming Zhao, Wei Zhou

{"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":"132 45","pages":"0"},"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}

引用次数: 0

Multiscale Pore Network Modeling and Flow Property Analysis for Tight Sandstone: A case study 致密砂岩多尺度孔隙网络建模与流动特性分析

3区地球科学

Journal of Geophysics and Engineering Pub Date : 2023-11-13 DOI: 10.1093/jge/gxad092

Xiang Wu, Fei Wang, Zhanshan Xiao, Yonghao Zhang, Jianbin Zhao, Chaoqiang Fang, Bo Wei

{"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":"132 35","pages":"0"},"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}

引用次数: 0

Rock physics characteristics and their control factors of carbonate in different sedimentary microfacies of the Yingshan Formation, Gucheng Area, Tarim Basin 塔里木盆地古城地区鹰山组不同沉积微相碳酸盐岩岩石物理特征及其控制因素

3区地球科学

Journal of Geophysics and Engineering Pub Date : 2023-11-08 DOI: 10.1093/jge/gxad087

Jiaqing Wang, Jixin Deng, Hui Xia, Longlong Yan

{"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":"27 6","pages":"0"},"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}

引用次数: 0

Adaptive focus beam migration method in visco-acoustic media 粘声介质中的自适应聚焦束偏移方法

3区地球科学

Journal of Geophysics and Engineering Pub Date : 2023-10-31 DOI: 10.1093/jge/gxad086

Kai Zhang, Yiming Pan, Yunyun Sang, Xuecheng Xu, Fankun Meng

引用次数: 0

Petrophysical properties identification and estimation of the Wufeng-Longmaxi shale gas reservoirs: A case study from South-West China 五峰—龙马溪页岩气储层物性识别与评价——以西南地区为例

3区地球科学

Journal of Geophysics and Engineering Pub Date : 2023-10-26 DOI: 10.1093/jge/gxad088

Or Aimon Brou Koffi Kablan, Tongjun Chen

{"title":"Petrophysical properties identification and estimation of the Wufeng-Longmaxi shale gas reservoirs: A case study from South-West China","authors":"Or Aimon Brou Koffi Kablan, Tongjun Chen","doi":"10.1093/jge/gxad088","DOIUrl":"https://doi.org/10.1093/jge/gxad088","url":null,"abstract":"Abstract Petrophysical properties are critical for shale gas reservoir characterization and simulation. The Wufeng-Longmaxi shale, in the southeastern margin of the Sichuan Basin, is identified as a complex reservoir due to its variability in lithification and geological mechanisms. Thus, determining its characteristics is challenging. Based on wireline logs and pressure data analysis, a shale reservoir was identified, and petrophysical properties were described to obtain parameters to build a reservoir simulation model. The properties include shale volume, sand porosity, net reservoir thickness, total and effective porosities, and water saturation. Total and effective porosities were calculated using density method. Shale volume was estimated by applying Clavier equation to gamma-ray responses. Sand porosity and net reservoir thickness were evaluated using Thomas–Stieber model, and Simandoux equation was used to compute water saturation. The results indicate that the reservoir is characterized by a relatively low porosity and high shale content, with shale unequally distributed in its laminated form (approximately 75%), dispersed (about 20%), and structural form (5%). This research workflow can efficiently evaluate shale reservoir parameters and provide a reliable approach for future reservoir development and fracture identification.","PeriodicalId":54820,"journal":{"name":"Journal of Geophysics and Engineering","volume":"33 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134909126","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}

引用次数: 0