Deep-image-prior (DIP) is a novel approach to solve ill-posed inverse problems whose solution is parametrized with an untrained deep neural network and cascaded with the forward modeling operator. A key component to the success of such a method is represented by the choice of the network architecture, which must act as a natural prior to the inverse problem at hand and provide a strong inductive bias towards the desired solution. Inspired by the close link between neural networks and iterative algorithms in classical optimization, we propose to apply an unrolled version of the gradient descent (GD) algorithm as our DIP network architecture, denoted as the deep-unrolling (DU) architecture. Each layer of the unrolled network comprises of two parts: the first part corresponds to the GD step of the data-fidelity term, whilst the second part, formed by a six-layer convolutional neural network (CNN), plays the role of a regularizer function. The proposed DU architecture is applied to the problem of image-domain least-squares migration (IDLSM) to invert migrated seismic images for their underlying reflectivity and denoted as DU-IDLSM. As such, the DU architecture parameterizes the reflectivity, and the input of each layer of the unrolled network is the reflectivity at the previous layer. Similar to the classical DIP approach, the parameters of the DU architecture are optimized in an unsupervised fashion by minimizing the data misfit function itself. Through experiments with a part of the Sigsbee2A model and a marine field dataset, we test the effectiveness of the DU-IDLSM approach and highlight two key benefits. Firstly, the DU architecture can effectively regularize the inversion process, resulting in reflectivity estimates with fewer artifacts and higher image resolution than those produced by conventional IDLSM approaches. Secondly, we show that DU-IDLSM can produce a qualitative measure of the uncertainty associated with the least-squares migration process.
{"title":"Deep-unrolling architecture for image-domain least-squares migration","authors":"Wei Zhang, Matteo Ravasi, Jinghuai Gao, Ying Shi","doi":"10.1190/geo2023-0428.1","DOIUrl":"https://doi.org/10.1190/geo2023-0428.1","url":null,"abstract":"Deep-image-prior (DIP) is a novel approach to solve ill-posed inverse problems whose solution is parametrized with an untrained deep neural network and cascaded with the forward modeling operator. A key component to the success of such a method is represented by the choice of the network architecture, which must act as a natural prior to the inverse problem at hand and provide a strong inductive bias towards the desired solution. Inspired by the close link between neural networks and iterative algorithms in classical optimization, we propose to apply an unrolled version of the gradient descent (GD) algorithm as our DIP network architecture, denoted as the deep-unrolling (DU) architecture. Each layer of the unrolled network comprises of two parts: the first part corresponds to the GD step of the data-fidelity term, whilst the second part, formed by a six-layer convolutional neural network (CNN), plays the role of a regularizer function. The proposed DU architecture is applied to the problem of image-domain least-squares migration (IDLSM) to invert migrated seismic images for their underlying reflectivity and denoted as DU-IDLSM. As such, the DU architecture parameterizes the reflectivity, and the input of each layer of the unrolled network is the reflectivity at the previous layer. Similar to the classical DIP approach, the parameters of the DU architecture are optimized in an unsupervised fashion by minimizing the data misfit function itself. Through experiments with a part of the Sigsbee2A model and a marine field dataset, we test the effectiveness of the DU-IDLSM approach and highlight two key benefits. Firstly, the DU architecture can effectively regularize the inversion process, resulting in reflectivity estimates with fewer artifacts and higher image resolution than those produced by conventional IDLSM approaches. Secondly, we show that DU-IDLSM can produce a qualitative measure of the uncertainty associated with the least-squares migration process.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianshen Gao, Ya-Ni Ma, Liming Jiang, Chunli Lu, Juncheng Shi
Electrical imaging logging in OBM (oil-based mud) has been developed for some time and is gradually playing an important role in the description of deep carbonate and shale reservoirs. Quantitative characterization of reservoir rock parameters such as resistivity is one of the most innovative developments in this field. The development of this technology needs to address and resolve four core issues: a wide range of parameter variations, removal of mud-cake influence in low-resistivity formations, dielectric rollover in high-resistivity formations, and multi-frequency dielectric dispersion effects. To address the aforementioned issues, the joint use of a BPNN (Backpropagation neural network) and the MPGA (multiple population genetic algorithm)-LM (Levenberg-Marquardt) algorithm for high-resolution quantitative imaging is proposed. First, using the theory of physics model-driven approach, numerical simulation is utilized to calculate the well logging response data under the influence of multiple parameters, thereby establishing a forward response database. Then, within the forward response database, the instrument response function is fitted using BPNN, to compress the data volume. Next, based on the fitted response function, an inversion method for three parameters, including reservoir rock resistivity, permittivity, and plate standoff, is established using the LM algorithm optimized with MPGA. The results indicate that the use of a three-layer BPNN enables rapid and accurate calculation of the electrical imaging logging response in OBM. The calculation of a single point only requires 0.1 ms with an accuracy of over 99%. The MPGA-LM algorithm exhibits stronger stability and improved inversion accuracy, with a single point inversion time of only 2 ms, and contributes to the high-definition quantitative description of electrical imaging logging in OBM, which is important in characterizing formation structures, distinguishing formation fractures etc.
{"title":"Research on Quantitative Inversion Characterization of High-Definition Electrical Imaging Logging in Oil-Based Mud Based on BPNN and MPGA-LM Algorithm","authors":"Jianshen Gao, Ya-Ni Ma, Liming Jiang, Chunli Lu, Juncheng Shi","doi":"10.1190/geo2023-0468.1","DOIUrl":"https://doi.org/10.1190/geo2023-0468.1","url":null,"abstract":"Electrical imaging logging in OBM (oil-based mud) has been developed for some time and is gradually playing an important role in the description of deep carbonate and shale reservoirs. Quantitative characterization of reservoir rock parameters such as resistivity is one of the most innovative developments in this field. The development of this technology needs to address and resolve four core issues: a wide range of parameter variations, removal of mud-cake influence in low-resistivity formations, dielectric rollover in high-resistivity formations, and multi-frequency dielectric dispersion effects. To address the aforementioned issues, the joint use of a BPNN (Backpropagation neural network) and the MPGA (multiple population genetic algorithm)-LM (Levenberg-Marquardt) algorithm for high-resolution quantitative imaging is proposed. First, using the theory of physics model-driven approach, numerical simulation is utilized to calculate the well logging response data under the influence of multiple parameters, thereby establishing a forward response database. Then, within the forward response database, the instrument response function is fitted using BPNN, to compress the data volume. Next, based on the fitted response function, an inversion method for three parameters, including reservoir rock resistivity, permittivity, and plate standoff, is established using the LM algorithm optimized with MPGA. The results indicate that the use of a three-layer BPNN enables rapid and accurate calculation of the electrical imaging logging response in OBM. The calculation of a single point only requires 0.1 ms with an accuracy of over 99%. The MPGA-LM algorithm exhibits stronger stability and improved inversion accuracy, with a single point inversion time of only 2 ms, and contributes to the high-definition quantitative description of electrical imaging logging in OBM, which is important in characterizing formation structures, distinguishing formation fractures etc.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139789051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep-image-prior (DIP) is a novel approach to solve ill-posed inverse problems whose solution is parametrized with an untrained deep neural network and cascaded with the forward modeling operator. A key component to the success of such a method is represented by the choice of the network architecture, which must act as a natural prior to the inverse problem at hand and provide a strong inductive bias towards the desired solution. Inspired by the close link between neural networks and iterative algorithms in classical optimization, we propose to apply an unrolled version of the gradient descent (GD) algorithm as our DIP network architecture, denoted as the deep-unrolling (DU) architecture. Each layer of the unrolled network comprises of two parts: the first part corresponds to the GD step of the data-fidelity term, whilst the second part, formed by a six-layer convolutional neural network (CNN), plays the role of a regularizer function. The proposed DU architecture is applied to the problem of image-domain least-squares migration (IDLSM) to invert migrated seismic images for their underlying reflectivity and denoted as DU-IDLSM. As such, the DU architecture parameterizes the reflectivity, and the input of each layer of the unrolled network is the reflectivity at the previous layer. Similar to the classical DIP approach, the parameters of the DU architecture are optimized in an unsupervised fashion by minimizing the data misfit function itself. Through experiments with a part of the Sigsbee2A model and a marine field dataset, we test the effectiveness of the DU-IDLSM approach and highlight two key benefits. Firstly, the DU architecture can effectively regularize the inversion process, resulting in reflectivity estimates with fewer artifacts and higher image resolution than those produced by conventional IDLSM approaches. Secondly, we show that DU-IDLSM can produce a qualitative measure of the uncertainty associated with the least-squares migration process.
{"title":"Deep-unrolling architecture for image-domain least-squares migration","authors":"Wei Zhang, Matteo Ravasi, Jinghuai Gao, Ying Shi","doi":"10.1190/geo2023-0428.1","DOIUrl":"https://doi.org/10.1190/geo2023-0428.1","url":null,"abstract":"Deep-image-prior (DIP) is a novel approach to solve ill-posed inverse problems whose solution is parametrized with an untrained deep neural network and cascaded with the forward modeling operator. A key component to the success of such a method is represented by the choice of the network architecture, which must act as a natural prior to the inverse problem at hand and provide a strong inductive bias towards the desired solution. Inspired by the close link between neural networks and iterative algorithms in classical optimization, we propose to apply an unrolled version of the gradient descent (GD) algorithm as our DIP network architecture, denoted as the deep-unrolling (DU) architecture. Each layer of the unrolled network comprises of two parts: the first part corresponds to the GD step of the data-fidelity term, whilst the second part, formed by a six-layer convolutional neural network (CNN), plays the role of a regularizer function. The proposed DU architecture is applied to the problem of image-domain least-squares migration (IDLSM) to invert migrated seismic images for their underlying reflectivity and denoted as DU-IDLSM. As such, the DU architecture parameterizes the reflectivity, and the input of each layer of the unrolled network is the reflectivity at the previous layer. Similar to the classical DIP approach, the parameters of the DU architecture are optimized in an unsupervised fashion by minimizing the data misfit function itself. Through experiments with a part of the Sigsbee2A model and a marine field dataset, we test the effectiveness of the DU-IDLSM approach and highlight two key benefits. Firstly, the DU architecture can effectively regularize the inversion process, resulting in reflectivity estimates with fewer artifacts and higher image resolution than those produced by conventional IDLSM approaches. Secondly, we show that DU-IDLSM can produce a qualitative measure of the uncertainty associated with the least-squares migration process.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Acoustic logging is one of the most promising methods for the quantitative evaluation of cement bond conditions in cased holes. However, inefficient utilization of full-wave information yields unsatisfactory interpretation accuracy. Fundamentally, this is because the wavefield characteristics have not been thoroughly investigated under various cement bonding conditions. Thus, this study derives analytical solutions of wavefields for a single-cased-hole model and emphasizes on the dispersion calculation algorithm. To solve the dispersion equation when solving for the poles of the propagating modes with real wavenumbers, we renormalize the Bessel function related to the borehole fluid by multiplying it with an attenuation factor. For leaky modes with complex wavenumbers, we propose a novel method to find peaks of the matrix condition number (LPMCN) in the frequency domain to determine dispersion poles, avoiding the local optimization issues resulting from the traditional GaussNewton iteration method. Combining these two methods, we establish a fast and accurate workflow for evaluating the dispersion of all modes in cased holes using a relatively fast bisection method to manage the dispersion of the propagating modes and employing the LPMCN method to derive dispersion curves of leaky modes. Furthermore, all propagating modes are individually investigated in the monopole measurement by evaluating residues of the real poles in a casing-free model. The analysis demonstrates that the first-order pseudo-Rayleigh wave (PR1) and inner Stoneley wave (ST1) are the two strongest modes. Finally, we focus on the waveforms and dispersion characteristics of the outer Stoneley wave (ST2) related to the fluid channel in the cement annulus. The results reveal that as the fluid thickness increases the phase velocity of the ST2 mode decreases, while its amplitude increases. Therefore, the ST2 mode can potentially evaluate the thickness of the fluid channel in a cement annulus if an effective weak-signal-extraction method is utilized.
{"title":"Analytical solutions for dispersions and waveforms of acoustic logging in a cased hole","authors":"Hua Wang, Tianlin Liu, Yunjia Ji","doi":"10.1190/geo2023-0316.1","DOIUrl":"https://doi.org/10.1190/geo2023-0316.1","url":null,"abstract":"Acoustic logging is one of the most promising methods for the quantitative evaluation of cement bond conditions in cased holes. However, inefficient utilization of full-wave information yields unsatisfactory interpretation accuracy. Fundamentally, this is because the wavefield characteristics have not been thoroughly investigated under various cement bonding conditions. Thus, this study derives analytical solutions of wavefields for a single-cased-hole model and emphasizes on the dispersion calculation algorithm. To solve the dispersion equation when solving for the poles of the propagating modes with real wavenumbers, we renormalize the Bessel function related to the borehole fluid by multiplying it with an attenuation factor. For leaky modes with complex wavenumbers, we propose a novel method to find peaks of the matrix condition number (LPMCN) in the frequency domain to determine dispersion poles, avoiding the local optimization issues resulting from the traditional GaussNewton iteration method. Combining these two methods, we establish a fast and accurate workflow for evaluating the dispersion of all modes in cased holes using a relatively fast bisection method to manage the dispersion of the propagating modes and employing the LPMCN method to derive dispersion curves of leaky modes. Furthermore, all propagating modes are individually investigated in the monopole measurement by evaluating residues of the real poles in a casing-free model. The analysis demonstrates that the first-order pseudo-Rayleigh wave (PR1) and inner Stoneley wave (ST1) are the two strongest modes. Finally, we focus on the waveforms and dispersion characteristics of the outer Stoneley wave (ST2) related to the fluid channel in the cement annulus. The results reveal that as the fluid thickness increases the phase velocity of the ST2 mode decreases, while its amplitude increases. Therefore, the ST2 mode can potentially evaluate the thickness of the fluid channel in a cement annulus if an effective weak-signal-extraction method is utilized.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reverse time migration (RTM) is an accurate method for imaging complex geologic structures without imposing any dip limitations. However, a large amount of high-amplitude, low-frequency noise, which is mainly generated by the crosscorrelation of source and receiver wavefields propagating in the same directions, seriously contaminates the image quality. The causal imaging condition with separated up- and downgoing wavefields is an effective approach to reduce these low-frequency artifacts. Explicit up- and downgoing wavefield decomposition based on the Hilbert transform is computationally expensive due to additional wavefield extrapolation and storage for the imaginary parts. Directionally propagating wavefield has distinctive kinematic patterns such as traveltime and wavefront curvature, which provides us an opportunity to implement the wavefield decomposition using the statistical neural network method. Using extrapolated wavefields as the input and the decomposed up-, down-, left- and rightgoing wavefields as the labeled data, we train a pair of generative adversarial networks to predict directional wavefields. The training datasets are generated using seismic full-waveform modeling and explicit wavefield decomposition based on the Hilbert transform. Then, the decomposed directional wavefields are incorporated into a novel imaging condition that depends on subsurface dip angles to compute the reflectivity perpendicular to reflectors. Numerical experiments demonstrate that the proposed method can produce accurate directional wavefield decomposition results and high-quality reflectivity images without low-wavenumber artifacts.
{"title":"GAN-enhanced directional seismic wavefield decomposition and its application in reverse-time migration","authors":"Jiaxing Sun, Jidong Yang, Jianping Huang, Youcai Yu, Yiwei Tian, Shanyuan Qin","doi":"10.1190/geo2023-0296.1","DOIUrl":"https://doi.org/10.1190/geo2023-0296.1","url":null,"abstract":"Reverse time migration (RTM) is an accurate method for imaging complex geologic structures without imposing any dip limitations. However, a large amount of high-amplitude, low-frequency noise, which is mainly generated by the crosscorrelation of source and receiver wavefields propagating in the same directions, seriously contaminates the image quality. The causal imaging condition with separated up- and downgoing wavefields is an effective approach to reduce these low-frequency artifacts. Explicit up- and downgoing wavefield decomposition based on the Hilbert transform is computationally expensive due to additional wavefield extrapolation and storage for the imaginary parts. Directionally propagating wavefield has distinctive kinematic patterns such as traveltime and wavefront curvature, which provides us an opportunity to implement the wavefield decomposition using the statistical neural network method. Using extrapolated wavefields as the input and the decomposed up-, down-, left- and rightgoing wavefields as the labeled data, we train a pair of generative adversarial networks to predict directional wavefields. The training datasets are generated using seismic full-waveform modeling and explicit wavefield decomposition based on the Hilbert transform. Then, the decomposed directional wavefields are incorporated into a novel imaging condition that depends on subsurface dip angles to compute the reflectivity perpendicular to reflectors. Numerical experiments demonstrate that the proposed method can produce accurate directional wavefield decomposition results and high-quality reflectivity images without low-wavenumber artifacts.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139850454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reverse time migration (RTM) is an accurate method for imaging complex geologic structures without imposing any dip limitations. However, a large amount of high-amplitude, low-frequency noise, which is mainly generated by the crosscorrelation of source and receiver wavefields propagating in the same directions, seriously contaminates the image quality. The causal imaging condition with separated up- and downgoing wavefields is an effective approach to reduce these low-frequency artifacts. Explicit up- and downgoing wavefield decomposition based on the Hilbert transform is computationally expensive due to additional wavefield extrapolation and storage for the imaginary parts. Directionally propagating wavefield has distinctive kinematic patterns such as traveltime and wavefront curvature, which provides us an opportunity to implement the wavefield decomposition using the statistical neural network method. Using extrapolated wavefields as the input and the decomposed up-, down-, left- and rightgoing wavefields as the labeled data, we train a pair of generative adversarial networks to predict directional wavefields. The training datasets are generated using seismic full-waveform modeling and explicit wavefield decomposition based on the Hilbert transform. Then, the decomposed directional wavefields are incorporated into a novel imaging condition that depends on subsurface dip angles to compute the reflectivity perpendicular to reflectors. Numerical experiments demonstrate that the proposed method can produce accurate directional wavefield decomposition results and high-quality reflectivity images without low-wavenumber artifacts.
{"title":"GAN-enhanced directional seismic wavefield decomposition and its application in reverse-time migration","authors":"Jiaxing Sun, Jidong Yang, Jianping Huang, Youcai Yu, Yiwei Tian, Shanyuan Qin","doi":"10.1190/geo2023-0296.1","DOIUrl":"https://doi.org/10.1190/geo2023-0296.1","url":null,"abstract":"Reverse time migration (RTM) is an accurate method for imaging complex geologic structures without imposing any dip limitations. However, a large amount of high-amplitude, low-frequency noise, which is mainly generated by the crosscorrelation of source and receiver wavefields propagating in the same directions, seriously contaminates the image quality. The causal imaging condition with separated up- and downgoing wavefields is an effective approach to reduce these low-frequency artifacts. Explicit up- and downgoing wavefield decomposition based on the Hilbert transform is computationally expensive due to additional wavefield extrapolation and storage for the imaginary parts. Directionally propagating wavefield has distinctive kinematic patterns such as traveltime and wavefront curvature, which provides us an opportunity to implement the wavefield decomposition using the statistical neural network method. Using extrapolated wavefields as the input and the decomposed up-, down-, left- and rightgoing wavefields as the labeled data, we train a pair of generative adversarial networks to predict directional wavefields. The training datasets are generated using seismic full-waveform modeling and explicit wavefield decomposition based on the Hilbert transform. Then, the decomposed directional wavefields are incorporated into a novel imaging condition that depends on subsurface dip angles to compute the reflectivity perpendicular to reflectors. Numerical experiments demonstrate that the proposed method can produce accurate directional wavefield decomposition results and high-quality reflectivity images without low-wavenumber artifacts.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139790722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Acoustic logging is one of the most promising methods for the quantitative evaluation of cement bond conditions in cased holes. However, inefficient utilization of full-wave information yields unsatisfactory interpretation accuracy. Fundamentally, this is because the wavefield characteristics have not been thoroughly investigated under various cement bonding conditions. Thus, this study derives analytical solutions of wavefields for a single-cased-hole model and emphasizes on the dispersion calculation algorithm. To solve the dispersion equation when solving for the poles of the propagating modes with real wavenumbers, we renormalize the Bessel function related to the borehole fluid by multiplying it with an attenuation factor. For leaky modes with complex wavenumbers, we propose a novel method to find peaks of the matrix condition number (LPMCN) in the frequency domain to determine dispersion poles, avoiding the local optimization issues resulting from the traditional GaussNewton iteration method. Combining these two methods, we establish a fast and accurate workflow for evaluating the dispersion of all modes in cased holes using a relatively fast bisection method to manage the dispersion of the propagating modes and employing the LPMCN method to derive dispersion curves of leaky modes. Furthermore, all propagating modes are individually investigated in the monopole measurement by evaluating residues of the real poles in a casing-free model. The analysis demonstrates that the first-order pseudo-Rayleigh wave (PR1) and inner Stoneley wave (ST1) are the two strongest modes. Finally, we focus on the waveforms and dispersion characteristics of the outer Stoneley wave (ST2) related to the fluid channel in the cement annulus. The results reveal that as the fluid thickness increases the phase velocity of the ST2 mode decreases, while its amplitude increases. Therefore, the ST2 mode can potentially evaluate the thickness of the fluid channel in a cement annulus if an effective weak-signal-extraction method is utilized.
{"title":"Analytical solutions for dispersions and waveforms of acoustic logging in a cased hole","authors":"Hua Wang, Tianlin Liu, Yunjia Ji","doi":"10.1190/geo2023-0316.1","DOIUrl":"https://doi.org/10.1190/geo2023-0316.1","url":null,"abstract":"Acoustic logging is one of the most promising methods for the quantitative evaluation of cement bond conditions in cased holes. However, inefficient utilization of full-wave information yields unsatisfactory interpretation accuracy. Fundamentally, this is because the wavefield characteristics have not been thoroughly investigated under various cement bonding conditions. Thus, this study derives analytical solutions of wavefields for a single-cased-hole model and emphasizes on the dispersion calculation algorithm. To solve the dispersion equation when solving for the poles of the propagating modes with real wavenumbers, we renormalize the Bessel function related to the borehole fluid by multiplying it with an attenuation factor. For leaky modes with complex wavenumbers, we propose a novel method to find peaks of the matrix condition number (LPMCN) in the frequency domain to determine dispersion poles, avoiding the local optimization issues resulting from the traditional GaussNewton iteration method. Combining these two methods, we establish a fast and accurate workflow for evaluating the dispersion of all modes in cased holes using a relatively fast bisection method to manage the dispersion of the propagating modes and employing the LPMCN method to derive dispersion curves of leaky modes. Furthermore, all propagating modes are individually investigated in the monopole measurement by evaluating residues of the real poles in a casing-free model. The analysis demonstrates that the first-order pseudo-Rayleigh wave (PR1) and inner Stoneley wave (ST1) are the two strongest modes. Finally, we focus on the waveforms and dispersion characteristics of the outer Stoneley wave (ST2) related to the fluid channel in the cement annulus. The results reveal that as the fluid thickness increases the phase velocity of the ST2 mode decreases, while its amplitude increases. Therefore, the ST2 mode can potentially evaluate the thickness of the fluid channel in a cement annulus if an effective weak-signal-extraction method is utilized.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianshen Gao, Ya-Ni Ma, Liming Jiang, Chunli Lu, Juncheng Shi
Electrical imaging logging in OBM (oil-based mud) has been developed for some time and is gradually playing an important role in the description of deep carbonate and shale reservoirs. Quantitative characterization of reservoir rock parameters such as resistivity is one of the most innovative developments in this field. The development of this technology needs to address and resolve four core issues: a wide range of parameter variations, removal of mud-cake influence in low-resistivity formations, dielectric rollover in high-resistivity formations, and multi-frequency dielectric dispersion effects. To address the aforementioned issues, the joint use of a BPNN (Backpropagation neural network) and the MPGA (multiple population genetic algorithm)-LM (Levenberg-Marquardt) algorithm for high-resolution quantitative imaging is proposed. First, using the theory of physics model-driven approach, numerical simulation is utilized to calculate the well logging response data under the influence of multiple parameters, thereby establishing a forward response database. Then, within the forward response database, the instrument response function is fitted using BPNN, to compress the data volume. Next, based on the fitted response function, an inversion method for three parameters, including reservoir rock resistivity, permittivity, and plate standoff, is established using the LM algorithm optimized with MPGA. The results indicate that the use of a three-layer BPNN enables rapid and accurate calculation of the electrical imaging logging response in OBM. The calculation of a single point only requires 0.1 ms with an accuracy of over 99%. The MPGA-LM algorithm exhibits stronger stability and improved inversion accuracy, with a single point inversion time of only 2 ms, and contributes to the high-definition quantitative description of electrical imaging logging in OBM, which is important in characterizing formation structures, distinguishing formation fractures etc.
{"title":"Research on Quantitative Inversion Characterization of High-Definition Electrical Imaging Logging in Oil-Based Mud Based on BPNN and MPGA-LM Algorithm","authors":"Jianshen Gao, Ya-Ni Ma, Liming Jiang, Chunli Lu, Juncheng Shi","doi":"10.1190/geo2023-0468.1","DOIUrl":"https://doi.org/10.1190/geo2023-0468.1","url":null,"abstract":"Electrical imaging logging in OBM (oil-based mud) has been developed for some time and is gradually playing an important role in the description of deep carbonate and shale reservoirs. Quantitative characterization of reservoir rock parameters such as resistivity is one of the most innovative developments in this field. The development of this technology needs to address and resolve four core issues: a wide range of parameter variations, removal of mud-cake influence in low-resistivity formations, dielectric rollover in high-resistivity formations, and multi-frequency dielectric dispersion effects. To address the aforementioned issues, the joint use of a BPNN (Backpropagation neural network) and the MPGA (multiple population genetic algorithm)-LM (Levenberg-Marquardt) algorithm for high-resolution quantitative imaging is proposed. First, using the theory of physics model-driven approach, numerical simulation is utilized to calculate the well logging response data under the influence of multiple parameters, thereby establishing a forward response database. Then, within the forward response database, the instrument response function is fitted using BPNN, to compress the data volume. Next, based on the fitted response function, an inversion method for three parameters, including reservoir rock resistivity, permittivity, and plate standoff, is established using the LM algorithm optimized with MPGA. The results indicate that the use of a three-layer BPNN enables rapid and accurate calculation of the electrical imaging logging response in OBM. The calculation of a single point only requires 0.1 ms with an accuracy of over 99%. The MPGA-LM algorithm exhibits stronger stability and improved inversion accuracy, with a single point inversion time of only 2 ms, and contributes to the high-definition quantitative description of electrical imaging logging in OBM, which is important in characterizing formation structures, distinguishing formation fractures etc.","PeriodicalId":509604,"journal":{"name":"GEOPHYSICS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139849204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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