【摘要】 地震多属性反演的目的是为储层预测提供丰富的基础资料。在实现方法上主要有线性方法和非线性方法,地震多属性非线性反演方法多采用神经网络、支持向量机等工具进行映射,其反演预测结果比线性方法更符合实际地质情况。将自然电位曲线作为地震多属性非线性反演的目标,首先,通过线性回归的方法,寻找用于反演自然电位曲线的最优地震属性组合;然后,选用多层前馈神经网络,进行地震多属性非线性反演,得到了三维自然电位数据体;最后,利用自然电位数据体沿层切片,清晰地展示了东营三角洲沙三段中亚段砂体的前积过程。更多还原

【Abstract】 The seismic attribute can be subtracted from seismic data, and then it is specially used for the measurement of seismic data in its geometry, dynamics or statistics feature. The purpose of seismic multi-attributes inversion is to provide reliable foundation data for reservoir prediction. There are two methods in forecasting well logging characteristics from seismic data: linear regression and nonlinear method. In nonlinear mode, neural network or support vector machine is trained, using the selected attributes as inputs. Seismic multi-attributes nonlinear inversion results match the geologic distribution regulation much more than linear regression. Taking spontaneous potential (SP) curve as the inversion goal, firstly, we can find the optimal seismic attributes for SP inversion by using linear regression. Secondly, the multi-attributes inversion is carried out by using the multilayer feed forward network (MLFN) based on these seismic attributes, and acquiring 3-D SP data volume. Finally, the progradation process of delta-front sandbody in Dongying delta are displayed clearly through seismic slice along the layer. The practical application results show that the proposed method has a good performance and is beneficial for further application and application.更多还原