Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecas...Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecasting.However,existing deep learning models frequently overlook the selective utilization of information from other production wells,resulting in suboptimal performance in long-term production forecasting across multiple wells.To achieve accurate long-term petroleum production forecast,we propose a spatial-geological perception graph convolutional neural network(SGP-GCN)that accounts for the temporal,spatial,and geological dependencies inherent in petroleum production.Utilizing the attention mechanism,the SGP-GCN effectively captures intricate correlations within production and geological data,forming the representations of each production well.Based on the spatial distances and geological feature correlations,we construct a spatial-geological matrix as the weight matrix to enable differential utilization of information from other wells.Additionally,a matrix sparsification algorithm based on production clustering(SPC)is also proposed to optimize the weight distribution within the spatial-geological matrix,thereby enhancing long-term forecasting performance.Empirical evaluations have shown that the SGP-GCN outperforms existing deep learning models,such as CNN-LSTM-SA,in long-term petroleum production forecasting.This demonstrates the potential of the SGP-GCN as a valuable tool for long-term petroleum production forecasting across multiple wells.展开更多
A supply chain resilience model is established based on the biological cellular resilience theory to analyze the impact of the supplier relationship on supply chain resilience. A scenario where the market demand is ch...A supply chain resilience model is established based on the biological cellular resilience theory to analyze the impact of the supplier relationship on supply chain resilience. A scenario where the market demand is changed suddenly by some undesired events is considered. The results reveal that enhancing collaboration with a more resilient supplier can significantly improve supply chain resilience and reduce supply chain losses. It is also found that enhancing the supplier relationship can significantly benefit supply chain resilience if the collaborative intensity is relatively low, and it has less effect if supply chain members have already collaborated closely. Thus, enhancing the supplier relationship to a limited intensity is a relatively effective and economic method to strengthen supply chain resilience.展开更多
基金funded by National Natural Science Foundation of China,grant number 62071491.
文摘Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecasting.However,existing deep learning models frequently overlook the selective utilization of information from other production wells,resulting in suboptimal performance in long-term production forecasting across multiple wells.To achieve accurate long-term petroleum production forecast,we propose a spatial-geological perception graph convolutional neural network(SGP-GCN)that accounts for the temporal,spatial,and geological dependencies inherent in petroleum production.Utilizing the attention mechanism,the SGP-GCN effectively captures intricate correlations within production and geological data,forming the representations of each production well.Based on the spatial distances and geological feature correlations,we construct a spatial-geological matrix as the weight matrix to enable differential utilization of information from other wells.Additionally,a matrix sparsification algorithm based on production clustering(SPC)is also proposed to optimize the weight distribution within the spatial-geological matrix,thereby enhancing long-term forecasting performance.Empirical evaluations have shown that the SGP-GCN outperforms existing deep learning models,such as CNN-LSTM-SA,in long-term petroleum production forecasting.This demonstrates the potential of the SGP-GCN as a valuable tool for long-term petroleum production forecasting across multiple wells.
基金The National Natural Science Foundation of China(No.71171050,71390333)the National Key Technology R&D Program of China during the 12th Five-Year Plan Period(No.2013BAD19B05)+1 种基金the Scientific Innovation Research of College Graduates in Jiangsu Province(No.CXZZ12_0107)the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1237)
文摘A supply chain resilience model is established based on the biological cellular resilience theory to analyze the impact of the supplier relationship on supply chain resilience. A scenario where the market demand is changed suddenly by some undesired events is considered. The results reveal that enhancing collaboration with a more resilient supplier can significantly improve supply chain resilience and reduce supply chain losses. It is also found that enhancing the supplier relationship can significantly benefit supply chain resilience if the collaborative intensity is relatively low, and it has less effect if supply chain members have already collaborated closely. Thus, enhancing the supplier relationship to a limited intensity is a relatively effective and economic method to strengthen supply chain resilience.
