Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficul...Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.展开更多
The estimated ultimate recovery(EUR)of shale gas wells is influenced by many factors,and the accurate prediction still faces certain challenges.As an artificial intelligence algorithm,deep learning yields notable adva...The estimated ultimate recovery(EUR)of shale gas wells is influenced by many factors,and the accurate prediction still faces certain challenges.As an artificial intelligence algorithm,deep learning yields notable advantages in nonlinear regression.Therefore,it is feasible to predict the EUR of shale gas wells based on a deep-learning algorithm.In this paper,according to geological evaluation data,hydraulic fracturing data,production data and EUR evaluation results of 282 wells in the WY shale gas field,a deep-learning-based algorithm for EUR evaluation of shale gas wells was designed and realized.First,the existing EUR evaluation methods of shale gas wells and the deep feedforward neural network algorithm was systematically analyzed.Second,the technical process of a deep-learning-based algorithm for EUR prediction of shale gas wells was designed.Finally,by means of real data obtained from the WY shale gas field,several different cases were applied to testify the validity and accuracy of the proposed approach.The results show that the EUR prediction with high accuracy.In addition,the results are affected by the variety and number of input parameters,the network structure and hyperparameters.The proposed approach can be extended to other shale fields using the similar technic process.展开更多
Paraffin deposition is a severe global problem during crude oil production and transportation.To inhibit the formation of paraffin deposits,the commonly used methods are mechanical cleaning,coating the pipe to provide...Paraffin deposition is a severe global problem during crude oil production and transportation.To inhibit the formation of paraffin deposits,the commonly used methods are mechanical cleaning,coating the pipe to provide a smooth surface and reduce paraffin adhesion,electric heating,ultrasonic and microbial treatments,the use of paraffin inhibitors,etc.Pipeline coatings not only have the advantages of simple preparation and broad applications,but also maintain a long-term efficient and stable effect.In recent years,important progress has been made in research on pipe coatings for mitigating and preventing paraffin deposition.Several novel superhydrophilic organogel coatings with low surface energy were successfully prepared by bionic design.This paper reviews different types of coatings for inhibiting wax deposition in the petroleum industry.The research prospects and directions of this rapidly developing field are also briefly discussed.展开更多
基金supported by the National Natural Science Foundation of China (42274144,42304122,and 41974155)the Key Research and Development Program of Shaanxi (2023-YBGY-076)+1 种基金the National Key R&D Program of China (2020YFA0713404)the China Uranium Industry and East China University of Technology Joint Innovation Fund (NRE202107)。
文摘Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.
基金supported by the funding of National Science and Technology Major Projects of China(2016ZX05037-006-005,2016ZX05037-006,2016ZX05035-004)。
文摘The estimated ultimate recovery(EUR)of shale gas wells is influenced by many factors,and the accurate prediction still faces certain challenges.As an artificial intelligence algorithm,deep learning yields notable advantages in nonlinear regression.Therefore,it is feasible to predict the EUR of shale gas wells based on a deep-learning algorithm.In this paper,according to geological evaluation data,hydraulic fracturing data,production data and EUR evaluation results of 282 wells in the WY shale gas field,a deep-learning-based algorithm for EUR evaluation of shale gas wells was designed and realized.First,the existing EUR evaluation methods of shale gas wells and the deep feedforward neural network algorithm was systematically analyzed.Second,the technical process of a deep-learning-based algorithm for EUR prediction of shale gas wells was designed.Finally,by means of real data obtained from the WY shale gas field,several different cases were applied to testify the validity and accuracy of the proposed approach.The results show that the EUR prediction with high accuracy.In addition,the results are affected by the variety and number of input parameters,the network structure and hyperparameters.The proposed approach can be extended to other shale fields using the similar technic process.
文摘Paraffin deposition is a severe global problem during crude oil production and transportation.To inhibit the formation of paraffin deposits,the commonly used methods are mechanical cleaning,coating the pipe to provide a smooth surface and reduce paraffin adhesion,electric heating,ultrasonic and microbial treatments,the use of paraffin inhibitors,etc.Pipeline coatings not only have the advantages of simple preparation and broad applications,but also maintain a long-term efficient and stable effect.In recent years,important progress has been made in research on pipe coatings for mitigating and preventing paraffin deposition.Several novel superhydrophilic organogel coatings with low surface energy were successfully prepared by bionic design.This paper reviews different types of coatings for inhibiting wax deposition in the petroleum industry.The research prospects and directions of this rapidly developing field are also briefly discussed.
