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展开更多
At present,electrode line impedance supervision(ELIS)based protection is widely used to detect faults on grounding electrode lines,which are indispensable elements of high-voltage direct current(HVDC)systems.The exist...At present,electrode line impedance supervision(ELIS)based protection is widely used to detect faults on grounding electrode lines,which are indispensable elements of high-voltage direct current(HVDC)systems.The existing theoretical analysis of measured impedance is based on lumped line model and the threshold value is generally set according to engineering experience,which have caused the dead zone problem and even accidents.Therefore,a study on measured impedance of ELIS-based protection and its threshold value selection method is carried out to solve this problem.In this study,the expressions of measured impedance under normal operation and fault conditions are deduced based on rigorous and accurate line model.Based on the expressions,the characteristics of the measured impedance are calculated and analyzed.With the characteristics of the measured impedance,the applicability of the protection with the traditional threshold value is further discussed and the distribution of the dead zone can be located.Then,the method to calculate the threshold value of ELIS-based protection is proposed.With a proper threshold value selected by the proposed method,the dead zone of ELIS-based protection is effectively eliminated,and the protection can identify all types of faults even with large transition resistances.Case studies on PSCAD/EMTDC have been conducted to verify the conclusion.展开更多
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
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(No.52025071)the Joint Funds of the National Natural Science Foundation of China(No.U1866205)。
文摘At present,electrode line impedance supervision(ELIS)based protection is widely used to detect faults on grounding electrode lines,which are indispensable elements of high-voltage direct current(HVDC)systems.The existing theoretical analysis of measured impedance is based on lumped line model and the threshold value is generally set according to engineering experience,which have caused the dead zone problem and even accidents.Therefore,a study on measured impedance of ELIS-based protection and its threshold value selection method is carried out to solve this problem.In this study,the expressions of measured impedance under normal operation and fault conditions are deduced based on rigorous and accurate line model.Based on the expressions,the characteristics of the measured impedance are calculated and analyzed.With the characteristics of the measured impedance,the applicability of the protection with the traditional threshold value is further discussed and the distribution of the dead zone can be located.Then,the method to calculate the threshold value of ELIS-based protection is proposed.With a proper threshold value selected by the proposed method,the dead zone of ELIS-based protection is effectively eliminated,and the protection can identify all types of faults even with large transition resistances.Case studies on PSCAD/EMTDC have been conducted to verify the conclusion.
