Fractures improve the reservoir space performance of oil and gas,and provide important channels for oil and gas migration.The degree of development and distribution of fractures affect the production volume and stability of tight sandstone reservoirs.Seismic attribute analysis is a common and effective method in reservoir fracture prediction;however,the relationship between seismic attributes and fractures is often multivariate,complex,and nonlinear.The results of single attribute analysis may be unstable and have multiple solutions,which make it difficult to predict reservoir fractures accurately.To predict reservoir fracture characteristics more comprehensively and accurately,a multi-attribute fusion method based on the Laplace pyramid (LP) algorithm and pulse-coupled neural network (PCNN) is proposed.Multiple single attributes which are sensitive to fractures were obtained based on seismic attribute analysis.To protect high frequency details,the LP algorithm decomposes each individual attribute into multi-scale spatial frequency bands.The powerful nonlinear processing function of the PCNN model is used to analyze the clustering characteristics of the decomposition data.When directly using the ignition frequency of each sampling point,attribute fusion shows one-sidedness and high sensitivity to edges.To avoid this,local entropy (LE),which represents statistical characteristics,is introduced to fuse each LP decomposition scales.The PCNN model has a powerful nonlinear processing function which couples the influence of surrounding neurons and suppresses redundant information within a single attribute.The final multi-attribute fusion result is obtained by reconstruction.The experimental results show that the proposed method can improve the signal-to-noise ratio,predict the fracture distribution characteristics more comprehensively and effectively,and delineate fracture boundaries more clearly.
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