In this study area the geological conditions are complicated and the effective sandstone is very heterogeneous.The sandstones are thin and lateral and vertical variations are large.We introduce multi-attribute fusion ...In this study area the geological conditions are complicated and the effective sandstone is very heterogeneous.The sandstones are thin and lateral and vertical variations are large.We introduce multi-attribute fusion technology based on pre-stack seismic data, pre-stack P-and S-wave inversion results,and post-stack attributes.This method not only can keep the fluid information contained in pre-stack seismic data but also make use of the high SNR characteristics of post-stack data.First,we use a one-step recursive method to get the optimal attribute combination from a number of attributes.Second,we use a probabilistic neural network method to train the nonlinear relationship between log curves and seismic attributes and then use the trained samples to find the natural gamma ray distribution in the Su-14 well block and improve the resolution of seismic data.Finally,we predict the effective reservoir distribution in the Su-14 well block.展开更多
In response to many multi-attribute decision-making(MADM)problems involved in chemical processes such as controller tuning,which suffer human's subjective preferential nature in human–computer interactions,a nove...In response to many multi-attribute decision-making(MADM)problems involved in chemical processes such as controller tuning,which suffer human's subjective preferential nature in human–computer interactions,a novel affective computing and preferential evolutionary solution is proposed to adapt human–computer interaction mechanism.Based on the stimulating response mechanism,an improved affective computing model is introduced to quantify decision maker's preference in selections of interactive evolutionary computing.In addition,the mathematical relationship between affective space and decision maker's preferences is constructed.Subsequently,a human–computer interactive preferential evolutionary algorithm for MADM problems is proposed,which deals with attribute weights and optimal solutions based on preferential evolution metrics.To exemplify applications of the proposed methods,some test functions and,emphatically,controller tuning issues associated with a chemical process are investigated,giving satisfactory results.展开更多
基金The National Natural Science Foundation of China and China Petroleum&Chemical Corporation Co-funded Project(Grant Nos 40839905 and 40739907)
文摘In this study area the geological conditions are complicated and the effective sandstone is very heterogeneous.The sandstones are thin and lateral and vertical variations are large.We introduce multi-attribute fusion technology based on pre-stack seismic data, pre-stack P-and S-wave inversion results,and post-stack attributes.This method not only can keep the fluid information contained in pre-stack seismic data but also make use of the high SNR characteristics of post-stack data.First,we use a one-step recursive method to get the optimal attribute combination from a number of attributes.Second,we use a probabilistic neural network method to train the nonlinear relationship between log curves and seismic attributes and then use the trained samples to find the natural gamma ray distribution in the Su-14 well block and improve the resolution of seismic data.Finally,we predict the effective reservoir distribution in the Su-14 well block.
基金Supported by the Fundamental Research Funds for the Central Universities(ZY1347and YS1404)
文摘In response to many multi-attribute decision-making(MADM)problems involved in chemical processes such as controller tuning,which suffer human's subjective preferential nature in human–computer interactions,a novel affective computing and preferential evolutionary solution is proposed to adapt human–computer interaction mechanism.Based on the stimulating response mechanism,an improved affective computing model is introduced to quantify decision maker's preference in selections of interactive evolutionary computing.In addition,the mathematical relationship between affective space and decision maker's preferences is constructed.Subsequently,a human–computer interactive preferential evolutionary algorithm for MADM problems is proposed,which deals with attribute weights and optimal solutions based on preferential evolution metrics.To exemplify applications of the proposed methods,some test functions and,emphatically,controller tuning issues associated with a chemical process are investigated,giving satisfactory results.
