Finding channel sandbodies is an important task in oil and gas exploration due to the importance of fluvial reservoirs. It is difficult to describe fluvial reservoirs in detail owing to their frequent changes and seri...Finding channel sandbodies is an important task in oil and gas exploration due to the importance of fluvial reservoirs. It is difficult to describe fluvial reservoirs in detail owing to their frequent changes and serious intersections, as well as limitations of S/N ratio and seismic data resolution. Based on the Laohekou 3D data in Shengli Oilfield, we analyze the general characteristics of fluvial reservoirs in this area, from which we find that they are characterized by strong amplitudes on seismic profiles, high continuity on time slices, and low frequency in the frequency domain. In addition, a cluster of strong string-bead- like reflections was found after color processing and detailed interpretation. To understand this observation, we conduct forward modeling to explain the mechanism. This provides a new way to identify ancient channels in similar areas. By using the multi-attribute fusion and RGB display techniques, channel incision is more obvious and the characteristics of the channel structures are manifested much better. Finally, we introduce and apply multi-wavelet detection technology to identify weaker fluvial reservoir signals.展开更多
It is challenging to theoretically predict the coefficient of thermal expansion(CTE)for binary AmBn crystals owing to the complexity of their crystal structures and computational procedures.Herein,the Pearson feature ...It is challenging to theoretically predict the coefficient of thermal expansion(CTE)for binary AmBn crystals owing to the complexity of their crystal structures and computational procedures.Herein,the Pearson feature selection method is utilized to identify nine key features associated closely with crystal structures,and a backpropagation neural network model with two hidden layers containing 24 and 15 neurons is adopted to achieve the optimal matching effect of the CTE,which is specifically optimized by the pelican optimization algorithm.Moreover,the black-box nature of the model is well elucidated by interpretability techniques of Shapley additive explanations(SHAP)and accumulated local effects(ALE),including the specific impact rules of each feature and the interaction effects between features on the CTE.It is found that the feature of average bond length contributes up to 27%,while low-influence features serve an important function in increasing prediction accuracy.The findings demonstrate the high efficiency and accuracy of the developed model for predicting the CTE of binary crystals.展开更多
Internet search giant Baidu won a second-level prize at the China 2015 National Science and Technology Awards for its technological advancement of machine translation in early January.
基金sponsored by The Science and Technology Research Project,Shengli Oilfield (Grant No. YKW1002)
文摘Finding channel sandbodies is an important task in oil and gas exploration due to the importance of fluvial reservoirs. It is difficult to describe fluvial reservoirs in detail owing to their frequent changes and serious intersections, as well as limitations of S/N ratio and seismic data resolution. Based on the Laohekou 3D data in Shengli Oilfield, we analyze the general characteristics of fluvial reservoirs in this area, from which we find that they are characterized by strong amplitudes on seismic profiles, high continuity on time slices, and low frequency in the frequency domain. In addition, a cluster of strong string-bead- like reflections was found after color processing and detailed interpretation. To understand this observation, we conduct forward modeling to explain the mechanism. This provides a new way to identify ancient channels in similar areas. By using the multi-attribute fusion and RGB display techniques, channel incision is more obvious and the characteristics of the channel structures are manifested much better. Finally, we introduce and apply multi-wavelet detection technology to identify weaker fluvial reservoir signals.
基金supported by the Key R&D Program of Shaanxi(No.2025CY-YBXM-145)the National Natural Science Foundation of China(No.62371366)+3 种基金the Central Guiding Local Science and Technology Development Funds of Guizhou(No.2024-034)the Open Project of Yunnan Precious Metals Laboratory Co.,Ltd.(No.YPML-2023050246)the Innovation Capability Support Program of Shaanxi(Nos.2023-CX-PT-30 and 2022TD-28)the Fundamental Research Funds for the Central Universities(No.ZYTS25232).
文摘It is challenging to theoretically predict the coefficient of thermal expansion(CTE)for binary AmBn crystals owing to the complexity of their crystal structures and computational procedures.Herein,the Pearson feature selection method is utilized to identify nine key features associated closely with crystal structures,and a backpropagation neural network model with two hidden layers containing 24 and 15 neurons is adopted to achieve the optimal matching effect of the CTE,which is specifically optimized by the pelican optimization algorithm.Moreover,the black-box nature of the model is well elucidated by interpretability techniques of Shapley additive explanations(SHAP)and accumulated local effects(ALE),including the specific impact rules of each feature and the interaction effects between features on the CTE.It is found that the feature of average bond length contributes up to 27%,while low-influence features serve an important function in increasing prediction accuracy.The findings demonstrate the high efficiency and accuracy of the developed model for predicting the CTE of binary crystals.
文摘Internet search giant Baidu won a second-level prize at the China 2015 National Science and Technology Awards for its technological advancement of machine translation in early January.
