| Tong61block oil reservoir，Wangjiagang oil feildjiave been efficiently exploited manyyears, and now all the development layers have been flooded in the a large areas. Oil field hasentered the period of high water with oil production. Crude oil in the underground is driveredby the water in the long-term, and cause a series of problems include complex floodrelationships, unpredictable groundwater distribution and salinity, and the ditfculities inremaining oil exploration and evaluation. The practice of development shows that after a highdegree of block oil exploitation，it still has remaining oil because of the geological structure,reservroir characteristics and current water development. In order to improve compliance ratesof interpretation and increase crude oil production, in the paper we study the blocks deeply,and build new methods and standards to identify block fluid property.We also use radial basisfunction (RBF) principles of neural network to identify flood layer and multi-well correlationinterpretation techniques. By combining with the dynamic test data, we analysis reservoirproperties vairation in this block arid propose optimal potential layer，and analysed remeningoil-rich region.Through analysis and research to the thesis, we gain the following achievements:(1) We certify interpretation parameters and establish reservoir parameters calculationmode】 based on standard logging data by using petrophysical experimental results.We alsoaccurately calculate the porosity, permeability, oil saturation and relative permeability andother parameters(2) We establish fluid flow identiifcation models and standards by combining test oiland logging data based on ‘relationship among four properties5study. To thin layers andinterbeded sand shale layers, we study impact of shale on its electrical and physical propertiesand establish shale correction methods，and then we caiculat the true resistivity, effective porosity and oil saturation.(3)We build water lfooded reservoir and water flooded level models based on test oiland logging data.,and use neural network analysis techniques to identify flooded layer,subdivision water lfooded level.We conduct detailed evaluation.on a single well using fluididentification and water flooded reservoir identification.(4)We,in the block^explained by use of multi-well,and combine with dynamicproduction data, block structure and reservoir fluid properties the discrepancy of porosityresults is less than1percent and the permeability discrepancy is less than quantity degree.(5)We analyze the block changes in physical properties, water distribution law bycalculating reservoir parameters and evaluation result and propose optimal potential layer tomake its t success rate of interpretion more than80percent.,and analysed reniening oil-richregion. |
