Prediction of seismic attenuation and dispersion that are inherently sensitive to hydraulic and elastic properties of the medium of interest in the presence of mesoscopic fractures and pores,is of great interest in th...Prediction of seismic attenuation and dispersion that are inherently sensitive to hydraulic and elastic properties of the medium of interest in the presence of mesoscopic fractures and pores,is of great interest in the characterization of fractured formations.This has been very difficult,however,considering that stress interactions between fractures and pores,related to their spatial distributions,tend to play a crucial role on affecting overall dynamic elastic properties that are largely unexplored.We thus choose to quantitatively investigate frequency-dependent P-wave characteristics in fractured porous rocks at the scale of a representative sample using a numerical scale-up procedure via performing finite element modelling.Based on 2-D numerical quasi-static experiments,effects of fracture and fluid properties on energy dissipation in response to wave-induced fluid flow at the mesoscopic scale are quantified via solving Biot's equations of consolidation.We show that numerical results are sensitive to some key characteristics of probed synthetic rocks containing unconnected and connected fractures,demonstrating that connectivity,aperture and inclination of fractures as well as fracture infills exhibit strong impacts on the two manifestations of WIFF mechanisms in the connected scenario,and on resulting total wave attenuation and phase velocity.This,in turn,illustrates the importance of these two WIFF mechanisms in fractured rocks and thus,a deeper understanding of them may eventually allow for a better characterization of fracture systems using seismic methods.Moreover,this presented work combines rock physics predictions with seismic numerical simulations in frequency domain to illustrate the sensitivity of seismic signatures on the monitoring of an idealized geologic CO_(2) sequestration in fractured reservoirs.The simulation demonstrates that these two WIFF mechanisms can strongly modify seismic records and hence,indicating that incorporating the two energy dissipation mechanisms in the geophysical interpretation can potentially improving the monitoring and surveying of fluid variations in fractured formations.展开更多
With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural network...With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.展开更多
Instability is an inherent problem with the attenuation compensation methods and has been partially relieved by using the inverse scheme.However,the conventional inversion-based attenuation compensation approaches ign...Instability is an inherent problem with the attenuation compensation methods and has been partially relieved by using the inverse scheme.However,the conventional inversion-based attenuation compensation approaches ignore the important prior information of the seismic dip.Thus,the compensated result appears to be distorted spatial continuity and has a low signal-to-noise ratio(S/N).To alleviate this issue,we have incorporated the seismic dip information into the inversion framework and have developed a dip-constrained attenuation compensation(DCAC)algorithm.The seismic dip information,calculated from the poststack seismic data,is the key to construct a dip constraint term.Benefiting from the introduction of the seismic dip constraint,the DCAC approach maintains the numerical stability and preserves the spatial continuity of the compensated result.Synthetic and field data examples demonstrate that the proposed method can not only improve seismic resolution,but also protect the continuity of seismic data.展开更多
Seismic impedance inversion is an important technique for structure identification and reservoir prediction.Model-based and data-driven impedance inversion are the commonly used inversion methods.In practice,the geoph...Seismic impedance inversion is an important technique for structure identification and reservoir prediction.Model-based and data-driven impedance inversion are the commonly used inversion methods.In practice,the geophysical inversion problem is essentially an ill-posedness problem,which means that there are many solutions corresponding to the same seismic data.Therefore,regularization schemes,which can provide stable and unique inversion results to some extent,have been introduced into the objective function as constrain terms.Among them,given a low-frequency initial impedance model is the most commonly used regularization method,which can provide a smooth and stable solution.However,this model-based inversion method relies heavily on the initial model and the inversion result is band limited to the effective frequency bandwidth of seismic data,which cannot effectively improve the seismic vertical resolution and is difficult to be applied to complex structural regions.Therefore,we propose a data-driven approach for high-resolution impedance inversion based on the bidirectional long short-term memory recurrent neural network,which regards seismic data as time-series rather than image-like patches.Compared with the model-based inversion method,the data-driven approach provides higher resolution inversion results,which demonstrates the effectiveness of the data-driven method for recovering the high-frequency components.However,judging from the inversion results for characterization the spatial distribution of thin-layer sands,the accuracy of high-frequency components is difficult to guarantee.Therefore,we add the model constraint to the objective function to overcome the shortages of relying only on the data-driven schemes.First,constructing the supervisor1 based on the bidirectional long short-term memory recurrent neural network,which provides the predicted impedance with higher resolution.Then,convolution constraint as supervisor2 is introduced into the objective function to guarantee the reliability and accuracy of the inversion results,which makes the synthetic seismic data obtained from the inversion result consistent with the input data.Finally,we test the proposed scheme based on the synthetic and field seismic data.Compared to model-based and purely data-driven impedance inversion methods,the proposed approach provides more accurate and reliable inversion results while with higher vertical resolution and better spatial continuity.The inversion results accurately characterize the spatial distribution relationship of thin sands.The model tests demonstrate that the model-constrained and data-driven impedance inversion scheme can effectively improve the thin-layer structure characterization based on the seismic data.Moreover,tests on the oil field data indicate the practicality and adaptability of the proposed method.展开更多
文摘Prediction of seismic attenuation and dispersion that are inherently sensitive to hydraulic and elastic properties of the medium of interest in the presence of mesoscopic fractures and pores,is of great interest in the characterization of fractured formations.This has been very difficult,however,considering that stress interactions between fractures and pores,related to their spatial distributions,tend to play a crucial role on affecting overall dynamic elastic properties that are largely unexplored.We thus choose to quantitatively investigate frequency-dependent P-wave characteristics in fractured porous rocks at the scale of a representative sample using a numerical scale-up procedure via performing finite element modelling.Based on 2-D numerical quasi-static experiments,effects of fracture and fluid properties on energy dissipation in response to wave-induced fluid flow at the mesoscopic scale are quantified via solving Biot's equations of consolidation.We show that numerical results are sensitive to some key characteristics of probed synthetic rocks containing unconnected and connected fractures,demonstrating that connectivity,aperture and inclination of fractures as well as fracture infills exhibit strong impacts on the two manifestations of WIFF mechanisms in the connected scenario,and on resulting total wave attenuation and phase velocity.This,in turn,illustrates the importance of these two WIFF mechanisms in fractured rocks and thus,a deeper understanding of them may eventually allow for a better characterization of fracture systems using seismic methods.Moreover,this presented work combines rock physics predictions with seismic numerical simulations in frequency domain to illustrate the sensitivity of seismic signatures on the monitoring of an idealized geologic CO_(2) sequestration in fractured reservoirs.The simulation demonstrates that these two WIFF mechanisms can strongly modify seismic records and hence,indicating that incorporating the two energy dissipation mechanisms in the geophysical interpretation can potentially improving the monitoring and surveying of fluid variations in fractured formations.
基金funded by the Fundamental Research Project of CNPC Geophysical Key Lab(2022DQ0604-4)the Strategic Cooperation Technology Projects of China National Petroleum Corporation and China University of Petroleum-Beijing(ZLZX 202003)。
文摘With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.
基金financial support provided by National Natural Science Foundation of China(42074141)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03)National Key R&D Program of China(2018YFA0702504)
文摘Instability is an inherent problem with the attenuation compensation methods and has been partially relieved by using the inverse scheme.However,the conventional inversion-based attenuation compensation approaches ignore the important prior information of the seismic dip.Thus,the compensated result appears to be distorted spatial continuity and has a low signal-to-noise ratio(S/N).To alleviate this issue,we have incorporated the seismic dip information into the inversion framework and have developed a dip-constrained attenuation compensation(DCAC)algorithm.The seismic dip information,calculated from the poststack seismic data,is the key to construct a dip constraint term.Benefiting from the introduction of the seismic dip constraint,the DCAC approach maintains the numerical stability and preserves the spatial continuity of the compensated result.Synthetic and field data examples demonstrate that the proposed method can not only improve seismic resolution,but also protect the continuity of seismic data.
基金funded by R&D Department of China National Petroleum Corporation(2022DQ0604-04)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03)the Science Research and Technology Development of PetroChina(2021DJ1206).
文摘Seismic impedance inversion is an important technique for structure identification and reservoir prediction.Model-based and data-driven impedance inversion are the commonly used inversion methods.In practice,the geophysical inversion problem is essentially an ill-posedness problem,which means that there are many solutions corresponding to the same seismic data.Therefore,regularization schemes,which can provide stable and unique inversion results to some extent,have been introduced into the objective function as constrain terms.Among them,given a low-frequency initial impedance model is the most commonly used regularization method,which can provide a smooth and stable solution.However,this model-based inversion method relies heavily on the initial model and the inversion result is band limited to the effective frequency bandwidth of seismic data,which cannot effectively improve the seismic vertical resolution and is difficult to be applied to complex structural regions.Therefore,we propose a data-driven approach for high-resolution impedance inversion based on the bidirectional long short-term memory recurrent neural network,which regards seismic data as time-series rather than image-like patches.Compared with the model-based inversion method,the data-driven approach provides higher resolution inversion results,which demonstrates the effectiveness of the data-driven method for recovering the high-frequency components.However,judging from the inversion results for characterization the spatial distribution of thin-layer sands,the accuracy of high-frequency components is difficult to guarantee.Therefore,we add the model constraint to the objective function to overcome the shortages of relying only on the data-driven schemes.First,constructing the supervisor1 based on the bidirectional long short-term memory recurrent neural network,which provides the predicted impedance with higher resolution.Then,convolution constraint as supervisor2 is introduced into the objective function to guarantee the reliability and accuracy of the inversion results,which makes the synthetic seismic data obtained from the inversion result consistent with the input data.Finally,we test the proposed scheme based on the synthetic and field seismic data.Compared to model-based and purely data-driven impedance inversion methods,the proposed approach provides more accurate and reliable inversion results while with higher vertical resolution and better spatial continuity.The inversion results accurately characterize the spatial distribution relationship of thin sands.The model tests demonstrate that the model-constrained and data-driven impedance inversion scheme can effectively improve the thin-layer structure characterization based on the seismic data.Moreover,tests on the oil field data indicate the practicality and adaptability of the proposed method.
