【摘要】 在进行储层预测和评价时 ,通常使用与储层预测有关的各种地震属性。以各种方法提取的一系列地震属性包含着丰富的地质信息 ,但有些属性可能彼此相关 ,这就造成信息的重复和冗余。由此可见 ,属性的无限增加也会给储层预测带来不利的影响。针对具体问题 ,从全体地震属性中挑选出最佳的地震属性子集是非常必要的 ,此即地震属性优化问题。其目的就是从众多地震属性中挑选出与研究目标关系最密切、反应最敏感的少数属性 ,再利用优化后的地震属性进行目标层储层参数 (如孔隙率、泥质含量和储层厚度等 )反演。本文主要讨论地震属性优化的遗传算法 (GA)与 BP神经网络相结合的 GA - BP方法 ,通过对大港探区 L JF区块三维地震资料的实际应用 ,取得了良好的地质效果更多还原

【Abstract】 When predicting and estimating reservoirs,we usually use kinds of seismic attributions which are related to reservoir prediction. Those seismic attributions extracted from kinds of methods include abundant geologic information,but some attributions are likely to correlate with one another,which results in repetition and redundancies of information. So the infinite increase of the number of attribution brings bad effects on reservoir prediction. To special problem,picking the best attribution subset from the whole seismic attributions is necessary,that is the optimization of seismic attributions,whose purpose is from numerous seismic attributions to choose small number of ones that are the most closely associated with and the most sensitive to the research target,and then we may carry out inversion of reservoir parameters of the target(such as porosity,content of mud,thickness of reservoir etc.) by using optimized seismic attributions. This paper mainly discusses the GA BP method combining the Genetic Ahgorithm of seismic attributions optimization with BP neural network,and we resulted in better geologic effect after applying it to the 3 D seismic data in LJF block of Dagang Oilfield.更多还原