基于粒子群优化的BP网络在地震属性融合技术中的应用

受地震资料品质、岩性、构造等诸多因素的影响，单一地震属性只能在一定程度上提供预测储层的方向，并存在多解性。地震属性融合技术用井孔资料对地震属性进行标定，建立储层含油气性与地震属性之间的关系，采取数学手段融合多种地震属性进行储层含油气性判别，避免了单一地震属性解释储层的多解性问题。BP网络具有良好的非线性拟合能力，但是易陷入局部极小值，不收敛，影响预测精度。针对该问题，采用粒子群优化其网络权值和阈值，再用BP网络对储层、非储层进行模式识别，取得较好成效。

Application of particle swarm optimization based BP neural network to multi-attribute fusion techniques

Constrained by factors such as quality of seism ic data，lithology and structures，single seism ic attribute can only be used to predict reservoirs to a certain extent and there aremultiple possibilities.Through calibrating seism ic attributeswith well data，seism ic attribute fusion techniques can correlate oil/gas potentialwith seism ic attributes.For oil/gas potential prediction of reservoirs，mathematics-based multi-attribute fusion can avoid the ambiguity of single attribute reservoir interpretation.Back propagation（BP）neural network is very good at nonlinear fitting，but it is easy to get a localm inimum without convergence，influencing the accuracy of prediction.To solve this problem，particle swarm is adopted first to optim ize the network weight and threshold，and BP neural network is then used to differentiate reservoirs and nonreservoirs.The results are satisfactory.