An Artificial Intelligent Analysis Method in Reservoir Development during High Water Cut Stage

High water and low oil production has been one of the major features of high water cut stage reservoir. In order to find a practical scheme to develop this kind of reservoir, an artificial intelligent analysis method by using statistical learning and clustering theory to evaluate and recombine rhythmite formations. The evaluating model was constructed by selecting appropriate indexes including permeability, variation coefficient of permeability, storage ability, moving ability, remaining recoverable reserves, oil mobility, water mobility and coefficient of flooding. The model can carry out intelligent analysis to some extent by taking advantage of its special merit of searching inner optimization. Therefore, it can find some valuable information from huge superficial data which is beyond people's analysis abilities. This action makes the result more pragmatic and valuable. Finally, application of the novel model on Z block in SL oilfield has been provided.