Reservoir Prediction Based On Random Forest Algorithm

Seismic attributes and fluid identification factors obtained from pre-stack and post-stack seismic data are widely used in reservoir prediction,such as fracture zone prediction,fluid identification etc.However,with the deepening of exploration and the increasing complexity of underground geological conditions,the problems of these methods have become increasingly prominent.(1)Many seismic attributes and fluid identification factors can be obtained from pre stack and post stack seismic data.But,it requires a lot of manual participation to select the seismic attributes and fluid identification factors that have a significant response to the reservoir characteristics of the studied area.(2)The prediction of reservoir only based on single seismic attribute or fluid identification factor usually leads to multiple solutions.To solve these problems,this paper introduces the random forest algorithm,which has the characteristics of small generalization error,strong anti-interference ability,and difficulty in overfitting.And it can effectively improve the accuracy and stability of storage layer prediction and fluid identification.The main research contents and understanding here are as follows:(1)The Taylor Median Theorem is used to simplify the computation in the decision tree generation process of random forest algorithm.The basic principles of the random forest algorithm are introduced by introducing the construction and execution processes of the forest.Besides,the classification performance of the random forest algorithm is verified by theoretical data and drilling data respectively,which would lay a foundation for multi parameter reservoir prediction.(2)The prediction of fracture zone based on random forest algorithm is studied.Firstly,based on the post-stack seismic data,the seismic attribute data volumes which can effectively represent the fracture zone in the study area are calculated.Then,based on the seismic attribute of the sidetrack and the interpretation result of the logging fracture,the characteristic parameters are selected,and the corresponding relationship between the seismic attribute and the fracture development zone degree is established.Finally,the random forest algorithm is used to comprehensively predict the fracture zone.Compared with well data and geological data,it can be found that under the support of random forest algorithm,multi-attribute(or parameter)input can be used to predict fracture zone,which can effectively reduce the multi solution of prediction.(3)The flow of reservoir fluid prediction based on random forest algorithm is established.First,based on the experimental verification results,five factors used for fluid identification are calculated by using drilling data,respectively,the longitudinal wave velocity ratio,Young's modulus,Poisson's ratio,Poisson's impedance and fluid properties.Then extract the characteristic parameters according to the drilling fluid distribution and establish the correspondence between the five fluid identification factors and the fluid distribution information.Then,based on pre-stack trace set,the lithology parameters such as reservoir density,P-wave and S-wave velocity are obtained respectively,and the fluid recognition factor was calculated by combining the relevant formula.Finally,the random forest algorithm is used to comprehensively discriminate the reservoir fluid.The example shows that the gas reservoir predicted by this method can be consistent with the well data,which indicates that the reservoir comprehensive prediction by using multiple fluid identification factors can effectively reduce the multiple solutions.