Multi-Parameter Fluid Identification And Applied Resarch

Now, with the AVO theory continuously improvementd and developmentd, particularly pre-stack synchronical three-parameter (velocity, shear velocity, density) inversion is matured. So we can access to other set of members directly through the three-parameter cut plane and physical relationship of rock fluid properties , because of a wide range of fluid parameters set, the fluid is indfferent, as it was difficult to choose the right according to the actual fluid property set members. Therefore it is necessary to carry out multi-parameter fluid identification reserch.The article have studied the variation of rock in different fluid properties which based on the analysis of physical rock theory.For a number of parameters shown in the different characteristics of fluid,we select the target layer parameters of high sensitivity followed the method of quantitative intersection , and take advantage of cross plots to analysis the sensitivity of parameter. Finally, research on fluid identifition based on BP neural network method, we use Castagna and Smith model comparison model to analysis the sensitivity of parameter. Using the actual logging data to verify multi-parameter identification results in the fluid ,then we can obtin the superiority of multi-parameter fluid identification.Petrophysical is a bridge which is linked between parameters of reservoir performance and seismic data, and geophysical data is necessary for inversion calculated and integrated interpretation of fluid identification. It will not only provide the knowledge based for the inversion and the necessary data, but also can reduce the uncertainty of seismic interpretation. The article describes the basics of rock physics and the theory of elastic parameters of impact factors, futher more introduced the two-phase medium theory and study the density and the shear wave velocity changed with the fluid saturation, and we predicted the fluid properties of difference in the different theories in different fluid saturation and porosity.For the various parameters in Seismic data processing and the parameters weight changing currently, we reference to a kind of quantitative technical to Select the sensitive parameters. Then we use the corss-plot to compare and verify the sensitivity of parameters. What we do is to avoid multi parameters forecasting involved in ambiguity and uncertainty that improve the multi-parameter analysis and application of technology.Research is based on BP neural network pattern recognition technology, we take advantage of sensitive parameters which we chose before, as net input, comparison,analysis of sensitivity parameters as input set of advantages. Then we establish the link between the reservoir of information to sensitive patameters, and predict the reservoir of the horizontal.At last, after the theoretical researching and analysising, we contact with oil field production and practices closely. We take use of a certain area of domestic well data for the case studying, using cross plots to verify and identify the reservoir fluid type ,and then we use neural network to identify and predict the unknown reservoir fluid.