Applied Geophysics最新文献_第10页

Seismic velocity inversion based on CNN-LSTM fusion deep neural network 基于CNN-LSTM融合深度神经网络的地震速度反演

IF 0.7 4区地球科学

Applied Geophysics Pub Date : 2022-05-02 DOI: 10.1007/s11770-021-0913-3

Cao Wei, Guo Xue-Bao, Tian Feng, Shi Ying, Wang Wei-Hong, Sun Hong-Ri, Ke Xuan

{"title":"Seismic velocity inversion based on CNN-LSTM fusion deep neural network","authors":"Cao Wei, Guo Xue-Bao, Tian Feng, Shi Ying, Wang Wei-Hong, Sun Hong-Ri, Ke Xuan","doi":"10.1007/s11770-021-0913-3","DOIUrl":"10.1007/s11770-021-0913-3","url":null,"abstract":"<div><p>Based on the CNN-LSTM fusion deep neural network, this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square (RMS) velocity and interval velocity from the common-midpoint (CMP) gather. In the proposed method, a convolutional neural network (CNN) Encoder and two long short-term memory networks (LSTMs) are used to extract spatial and temporal features from seismic signals, respectively, and a CNN Decoder is used to recover RMS velocity and interval velocity of underground media from various feature vectors. To address the problems of unstable gradients and easily fall into a local minimum in the deep neural network training process, we propose to use Kaiming normal initialization with zero negative slopes of rectified units and to adjust the network learning process by optimizing the mean square error (MSE) loss function with the introduction of a freezing factor. The experiments on testing dataset show that CNN-LSTM fusion deep neural network can predict RMS velocity as well as interval velocity more accurately, and its inversion accuracy is superior to that of single neural network models. The predictions on the complex structures and Marmousi model are consistent with the true velocity variation trends, and the predictions on field data can effectively correct the phase axis, improve the lateral continuity of phase axis and quality of stack section, indicating the effectiveness and decent generalization capability of the proposed method.</p></div>","PeriodicalId":55500,"journal":{"name":"Applied Geophysics","volume":"18 4","pages":"499 - 514"},"PeriodicalIF":0.7,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4098502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Model-data-driven AVO inversion method based on multiple objective functions 基于多目标函数的模型数据驱动AVO反演方法

IF 0.7 4区地球科学

Applied Geophysics Pub Date : 2022-05-02 DOI: 10.1007/s11770-021-0915-1

Sun Yu-Hang, Liu Yang

引用次数: 0

Full waveform inversion based on deep learning and optimal nearly analytic discrete method 基于深度学习和最优近解析离散方法的全波形反演

IF 0.7 4区地球科学

Applied Geophysics Pub Date : 2022-05-02 DOI: 10.1007/s11770-021-0912-4

Lu Fan, Zhou Yan-Jie, He Xi-Jun, Ma Xiao, Huang Xue-Yuan

{"title":"Full waveform inversion based on deep learning and optimal nearly analytic discrete method","authors":"Lu Fan, Zhou Yan-Jie, He Xi-Jun, Ma Xiao, Huang Xue-Yuan","doi":"10.1007/s11770-021-0912-4","DOIUrl":"10.1007/s11770-021-0912-4","url":null,"abstract":"<div><p>In this study, we implement forward modeling and inversion based on deep-learning strategies using an optimal nearly analytic discrete (ONAD) method. The forward-modeling method combines the ONAD method with recurrent neural network (RNN) for the first time. RNN is a type of neural network that is suitable for sequential data, which uses information from both previous and current times to obtain output information. We express the ONAD method using an RNN framework to advance the time iteration of an acoustic equation. This process can simplify programming using RNN and convolution kernels. Next, we use deep learning based on the proposed forward-modeling method to study full waveform-inversion problems. Because the main purpose of inversion is to minimize the error between real and synthetic data, inversion is essentially an optimization problem. Many new optimizers are available in the framework of deep learning, such as the Adam and Nadam optimizers, which are used for optimizing velocity model in the inversion process. We perform six numerical experiments. The first two experiments demonstrate the forward-modeling results, which indicate that the forward-modeling method can effectively suppress numerical dispersion and improve computational efficiency. The other four experiments demonstrate the inversion results, which show that the method proposed in this paper can effectively realize inversion imaging. We compare several optimizers used in deep learning and find that the Nadam optimizer has faster convergence and better effectiveness based on the ONAD method combined with RNN.</p></div>","PeriodicalId":55500,"journal":{"name":"Applied Geophysics","volume":"18 4","pages":"483 - 498"},"PeriodicalIF":0.7,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4098194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Q estimation using multifrequency point average method based on the Taylor series expansion with a different order 基于不同阶次泰勒级数展开的多频点平均Q估计

IF 0.7 4区地球科学

Applied Geophysics Pub Date : 2022-05-02 DOI: 10.1007/s11770-021-0918-y

Zhang Jin, Wang Yan-Guo, Zhang Guo-Shu, Lan Hui-Tian, Zhang Hua, Hao Ya-Ju

{"title":"Q estimation using multifrequency point average method based on the Taylor series expansion with a different order","authors":"Zhang Jin, Wang Yan-Guo, Zhang Guo-Shu, Lan Hui-Tian, Zhang Hua, Hao Ya-Ju","doi":"10.1007/s11770-021-0918-y","DOIUrl":"10.1007/s11770-021-0918-y","url":null,"abstract":"<div><p>The quality factor <i>Q</i> is an important parameter because it can reflect the reservoir attenuated features and can be used for inverse-<i>Q</i> filtering to compensate for the seismic wave energy. The accuracy of the <i>Q</i> estimation is greatly significant for improving the precision of the reservoir prediction and the resolution of seismic data. In this paper, the <i>Q</i> estimation formulas of the single-frequency point are derived on the basis of a different-order Taylor series expansion of the amplitude attenuated factor. Moreover, the multifrequency point average (MFPA) method is introduced to obtain a stable <i>Q</i> estimation. The model tests demonstrate that the MFPA method is less affected by the frequency band, travel time difference, time window width, and noise interference than the logical spectrum ratio (LSR) method and the energy ratio (ER) method and has a higher <i>Q</i> estimation accuracy. In addition, the proposed method can be applied to post-stack seismic data and obtain effective <i>Q</i> values of complex models. When the MFPA method was applied to real marine seismic data, the <i>Q</i> values estimated by the MFPA method with the 1st–4th order showed good consistency with each other. In contrast, the <i>Q</i> values obtained by the ER method were larger than those of the proposed method, while those estimated by the LSR method significantly deviated from the average values. In conclusion, the MFPA method has superior stability and practicability for the <i>Q</i> estimation.</p></div>","PeriodicalId":55500,"journal":{"name":"Applied Geophysics","volume":"18 4","pages":"557 - 568"},"PeriodicalIF":0.7,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11770-021-0918-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4096671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

3D Step-by-step inversion strategy for audio magnetotellurics data based on unstructured mesh 基于非结构化网格的音频大地电磁数据三维分步反演策略

IF 0.7 4区地球科学

Applied Geophysics Pub Date : 2022-02-17 DOI: 10.1007/s11770-021-0905-3

San Cheng, Zhi-Yong Zhang, Feng Zhou, Man Li, Hui Chen, Fu-Sheng Shi, Lin-Pin Huang, Yong Li

{"title":"3D Step-by-step inversion strategy for audio magnetotellurics data based on unstructured mesh","authors":"San Cheng, Zhi-Yong Zhang, Feng Zhou, Man Li, Hui Chen, Fu-Sheng Shi, Lin-Pin Huang, Yong Li","doi":"10.1007/s11770-021-0905-3","DOIUrl":"10.1007/s11770-021-0905-3","url":null,"abstract":"<div><p>A three-dimensional (3D) step-by-step inversion strategy for audio magnetotellurics (AMT) is investigated in this study. The objective function is minimized by iteratively solving the Gauss-Newton normal equation, and the inversion region is discretized with unstructured tetrahedral elements. The inversion proceeds step-by-step from a coarse mesh to a fine mesh. In the inversion iteration process, a mesh is adaptively optimized according to the spatial gradient information about the model resistivity to fine delineate the boundaries of abnormal bodies. In the early stage of inversion execution, a coarse mesh is used for inversion, and the inversion stability is improved by reducing the number of inversion elements. In addition, mesh refinement is performed in the iterative inversion process. The inversion results obtained from the previous mesh are used as the reference and initial models for the next mesh iterative inversion. The step-by-step inversion strategy can ensure that the inversion is performed in the correct direction, improving the inversion stability and results gradually. Synthetic results show that the step-by-step inversion strategy with a Gauss-Newton method for 3D AMT inversion is stable and reliable, which lays a foundation for further practical 3D AMT data inversion.</p></div>","PeriodicalId":55500,"journal":{"name":"Applied Geophysics","volume":"18 3","pages":"375 - 385"},"PeriodicalIF":0.7,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4680215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Rock physics inversion based on an optimized MCMC method 基于优化MCMC方法的岩石物理反演

IF 0.7 4区地球科学

Applied Geophysics Pub Date : 2022-02-17 DOI: 10.1007/s11770-021-0900-8

Jia-Jia Zhang, Hong-Bing Li, Guang-Zhi Zhang, Yi-Peng Gu, Zhuo-Fan Liu

{"title":"Rock physics inversion based on an optimized MCMC method","authors":"Jia-Jia Zhang, Hong-Bing Li, Guang-Zhi Zhang, Yi-Peng Gu, Zhuo-Fan Liu","doi":"10.1007/s11770-021-0900-8","DOIUrl":"10.1007/s11770-021-0900-8","url":null,"abstract":"<div><p>Rock physics inversion is to use seismic elastic properties of underground strata for predicting reservoir petrophysical parameters. The Markov chain Monte Carlo (MCMC) algorithm is commonly used to solve rock physics inverse problems. However, all the parameters to be inverted are iterated simultaneously in the conventional MCMC algorithm. What is obtained is an optimal solution of combining the petrophysical parameters with being inverted. This study introduces the alternating direction (AD) method into the MCMC algorithm (i.e. the optimized MCMC algorithm) to ensure that each petrophysical parameter can get the optimal solution and improve the convergence of the inversion. Firstly, the Gassmann equations and Xu-White model are used to model shaly sandstone, and the theoretical relationship between seismic elastic properties and reservoir petrophysical parameters is established. Then, in the framework of Bayesian theory, the optimized MCMC algorithm is used to generate a Markov chain to obtain the optimal solution of each physical parameter to be inverted and obtain the maximum posterior density of the physical parameter. The proposed method is applied to actual logging and seismic data and the results show that the method can obtain more accurate porosity, saturation, and clay volume.</p></div>","PeriodicalId":55500,"journal":{"name":"Applied Geophysics","volume":"18 3","pages":"288 - 298"},"PeriodicalIF":0.7,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4677122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reconstruction method of irregular seismic data with adaptive thresholds based on different sparse transform bases 基于不同稀疏变换基的不规则地震数据自适应阈值重建方法

IF 0.7 4区地球科学

Applied Geophysics Pub Date : 2022-02-17 DOI: 10.1007/s11770-021-0903-5

Hu Zhao, Tun Yang, Yu-Dong Ni, Xing-Gang Liu, Yin-Po Xu, Yi-Lei Zhang, Guang-Rong Zhang

{"title":"Reconstruction method of irregular seismic data with adaptive thresholds based on different sparse transform bases","authors":"Hu Zhao, Tun Yang, Yu-Dong Ni, Xing-Gang Liu, Yin-Po Xu, Yi-Lei Zhang, Guang-Rong Zhang","doi":"10.1007/s11770-021-0903-5","DOIUrl":"10.1007/s11770-021-0903-5","url":null,"abstract":"<div><p>Oil and gas seismic exploration have to adopt irregular seismic acquisition due to the increasingly complex exploration conditions to adapt to complex geological conditions and environments. However, the irregular seismic acquisition is accompanied by the lack of acquisition data, which requires high-precision regularization. The sparse signal feature in the transform domain in compressed sensing theory is used in this paper to recover the missing signal, involving sparse transform base optimization and threshold modeling. First, this paper analyzes and compares the effects of six sparse transformation bases on the reconstruction accuracy and efficiency of irregular seismic data and establishes the quantitative relationship between sparse transformation and reconstruction accuracy and efficiency. Second, an adaptive threshold modeling method based on sparse coefficient is provided to improve the reconstruction accuracy. Test results show that the method has good adaptability to different seismic data and sparse transform bases. The f-x domain reconstruction method of effective frequency samples is studied to address the problem of low computational efficiency. The parallel computing strategy of curvelet transform combined with OpenMP is further proposed, which substantially improves the computational efficiency under the premise of ensuring the reconstruction accuracy. Finally, the actual acquisition data are used to verify the proposed method. The results indicate that the proposed method strategy can solve the regularization problem of irregular seismic data in production and improve the imaging quality of the target layer economically and efficiently.</p></div>","PeriodicalId":55500,"journal":{"name":"Applied Geophysics","volume":"18 3","pages":"345 - 360"},"PeriodicalIF":0.7,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4678166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Integrating the geology, seismic attributes, and production of reservoirs to adjust interwell areas: A case from the Mangyshlak Basin of West Kazakhstan 结合储层地质、地震属性和产量调整井间面积——以哈萨克斯坦西部Mangyshlak盆地为例

IF 0.7 4区地球科学

Applied Geophysics Pub Date : 2022-02-17 DOI: 10.1007/s11770-021-0907-1

Zhumabekov Arslan, Liu Zhen, Portnov Vasily, Wei Xiaodong, Chen Xin

{"title":"Integrating the geology, seismic attributes, and production of reservoirs to adjust interwell areas: A case from the Mangyshlak Basin of West Kazakhstan","authors":"Zhumabekov Arslan, Liu Zhen, Portnov Vasily, Wei Xiaodong, Chen Xin","doi":"10.1007/s11770-021-0907-1","DOIUrl":"10.1007/s11770-021-0907-1","url":null,"abstract":"<div><p>Dynamic models of the seismic, geological, and flow characteristics of a reservoir are the main tool used to evaluate the potential of drilling new infill wells. Static geological models are mainly based on borehole data combined with dynamic analyses of production dynamics. They are used to determine the redevelopment of and adjustments to new drilling locations; however, such models rarely incorporate seismic data. Consequently, it is difficult to control the changes in geological models between wells, which results in the configuration of well positions and predicted results being less than ideal. To improve the development of adjusted areas in terms of their remaining oil contents, we developed a new integrated analysis that combines static sediment modelling, including microfacies analysis (among other reservoir and seismic properties), with production behaviours. Here, we illustrate this new process by (1) establishing favourable areas for static geological analysis; (2) studying well recompletion potential and the condition of non-producing wells; (3) conducting interwell analyses with seismic and sedimentary data; (4) identifying potential sites constrained by seismic and geological studies, as well as initial oilfield production; (5) providing suggestions in a new well development plan.</p></div>","PeriodicalId":55500,"journal":{"name":"Applied Geophysics","volume":"18 3","pages":"420 - 430"},"PeriodicalIF":0.7,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11770-021-0907-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4677126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Forward modeling of tight sandstone permeability based on mud intrusion depth and its application in the south of the Ordos Basin 基于泥侵深度的致密砂岩渗透率正演模拟及其在鄂尔多斯盆地南部的应用

IF 0.7 4区地球科学

Applied Geophysics Pub Date : 2022-02-17 DOI: 10.1007/s11770-021-0899-x

Wen-hui Liu, Xiao-Chun Lv, Bo Shen

{"title":"Forward modeling of tight sandstone permeability based on mud intrusion depth and its application in the south of the Ordos Basin","authors":"Wen-hui Liu, Xiao-Chun Lv, Bo Shen","doi":"10.1007/s11770-021-0899-x","DOIUrl":"10.1007/s11770-021-0899-x","url":null,"abstract":"<div><p>Permeability is an important index in reservoir evaluation, oil and gas accumulation control, and production efficiency. At present, permeability can be obtained through several methods. However, these methods are not suitable for tight sandstone in general because the pore type in tight sandstone is mainly secondary pores and has the characteristics of low porosity and permeability, high capillary pressure, and high irreducible water saturation. Mud invasion depth is closely related to permeability during drilling. In general, the greater the permeability, the shallower the mud invasion depth, and the smaller the permeability, the deeper the mud invasion depth. Therefore, this paper builds a model to predict the permeability of tight sandstone using mud invasion depth. The model is based on the improvement of the Darcy flow equation to obtain permeability using mud invasion depth inversion of array induction logging. The influence of various permeability factors on the model is analyzed by numerical simulation. The model is used to predict the permeability of tight sandstone in the south of the Ordos Basin. The predicted permeability is highly consistent with the core analysis permeability, which verifies the reliability of the method.</p></div>","PeriodicalId":55500,"journal":{"name":"Applied Geophysics","volume":"18 3","pages":"277 - 287"},"PeriodicalIF":0.7,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11770-021-0899-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4679286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new approach for calculating the apparent resistivity tensor 一种计算视电阻率张量的新方法

IF 0.7 4区地球科学

Applied Geophysics Pub Date : 2022-02-17 DOI: 10.1007/s11770-021-0906-2

Cong Zhou, Jing-Tian Tang, Yuan Yuan, Zheng-Yong Ren, Yong Li

{"title":"A new approach for calculating the apparent resistivity tensor","authors":"Cong Zhou, Jing-Tian Tang, Yuan Yuan, Zheng-Yong Ren, Yong Li","doi":"10.1007/s11770-021-0906-2","DOIUrl":"10.1007/s11770-021-0906-2","url":null,"abstract":"<div><p>The apparent resistivity tensor ρ<sub><i>B</i></sub> is an electromagnetic transfer function, which can be used to analyze and explain the underground electrical structure. Conventional method for obtaining the parameter requires controlled sources and can be easy to be disturbed by cultural noises. We present a new method for calculating the apparent resistivity tensor, the current density is first obtained by measuring the curl operator of the magnetic field on the Earth’s surface. This approach is independent of the assumption of a plane wave, and may be used irrespective of source types and field areas. We derived the analytical expressions of the apparent resistivity tensor based on synthetic horizontally layered models with a vertical magnetic dipole source. We then calculate the responses of ρ<sub><i>B</i></sub> through numerical modeling examples using both natural sources and controlled sources. Compared to traditional apparent resistivity definitions, our apparent resistivity tensor has the same amplitude value but with more sensitive phases in the far zone, and shows few distortions in the transition zone. And in the near-field zone, it is closer to the resistivity distribution under the ground. The simulation results have demonstrated the effectiveness of the proposed method for calculating the apparent resistivity tensor.</p></div>","PeriodicalId":55500,"journal":{"name":"Applied Geophysics","volume":"18 3","pages":"386 - 395"},"PeriodicalIF":0.7,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"4679707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0