基于PNN神经网络的地震属性反演技术

Attribute Seismic Inversions Based on PNN Neural Network

在薄互层地震储层预测中,属性分析和地震反演是当前应用地震资料进行储层预测的主要技术,两者实质上都属于反问题范畴,两者各有优缺点。提出了一种基于PNN神经网络的多属性地震反演技术,可以比较好地发挥两者的优势。概率神经网络(PPN)是一种数学内插方案,只不过在实现时利用了神经网络的架构,可以通过数学公式理解它的行为,克服了BP网络的的"黑匣子"问题。该技术在GTZ扶杨油层的砂岩预测中应用效果较好,厚度大于3 m的砂岩识别符合率超过90%以上。

At present,in the seismic reservoir predictions for thin interbeds,attribute analysis and seismic inversion are the main techniques to predict reservoirs by using seismic data.Both of the techniques intrinsically belong to the inverse problem domain and have their own advantages and shortcomings.A attribute seismic inversion is presented based on PNN neural network that can develop the advantages of the techniques mentioned above.Altough PNN neural network that is mathematical interpolation scheme is only use framework of neural network,its behavior by mathematical formula to overcome ＂black box＂ of BP neural network can be understanded.The application of the technique obtained a good effect in the sandstone predictions in GTZ Fuyang oil layer,and the identification coincident rate to the sandstone,whose thickness is over 3 m,is above 90%.