Study on the PNN Neural Network In ersion based on the Integration of Seismic Multi-attribute with Frequency Di ision RGB-Fuyu Oil Layer in Gaotaizi Area, Songliao Basin is taken for an Example

摘要

Fuyu oil layer in Changyuan of Daqing is a strategic area for Daqing oil field to increase and stabilize production at present, but its reservoir is structured mostly by riverway sand bodies. The sand bodies are thin and change with complexity, so they are difficult to identify. Through PNN probability neural network, and combined with the seismic multi-attribute and earthquake frequency division data, wave impedance inversions are implemented, and the maximum correlation coefficient is 0.517 if a single attribute is described and it is raised to 0.773 by linear weighting, and also 9 independent attributes are used for PNN network training so that a more complex nonlinear relationship is obtained. Then, the correlation coefficient continues to increase until 0.869, so that the spatial distribution characteristics of the sandstones in the study area are accurately described. This indicates that the PNN neural network inversion based on the integration of seismic multi-attribute with frequency division RGB can play an obvious effect in the areas of thin and changing sand bodies, and can meet the needs of the oil field exploration and development.