Research On Granularity Symbol Character Of BeiEr Depression Reservoir Sedimentary Environment

What this paper aims to do is to set up a set of granularity mixed distribution standard that can be consulted, based on the laser method of granularity distribution test and the disposal process of granularity statistics, set up on the operational platform of PPCR, contraposing the real environment of the granularity analysis of BeiEr Depression clastic rock of Hailaer Basin. This standard can promote the new indicated method of granularity to the applied stage as soon as possible. It can also make the granularity analysis statistics serve better in the geological research of oil exploration and development.This paper made the research on the indicated granularity of reservoirs of Hailaer Basin by describing and testing samples,disposing statistics,setting up matrix model and identifying pattern. This research gave priority to Beier Depression and Wuerxun Depression and obtained some results and cognition as followed: The first is combinating with the work of the building model of granularity parent population distribution of reservoirs of Beier Depression in Hailaer basin, a suit of identifying software PPCR was purchased. This software is much more powerful in functions and operations than GPER, and was applied successfully in the building model of mixed parent population distribution in this research. The second is that we set up the distribution of granularity parent population and transcendent sedimentary environment knowledge storeroom which including 254 distributional models of granularity matrix on the new software platform of PPCR by filtering,settling,regulating and disposing the granularity statistics of laser method in resent years, using the conclusions of sedimentary environmental explanation and of granularity matrix distributional statistics of 34 wells ,1055 pieces of sample of Beier Depression in Hailaer basin. This knowledge storeroom is used on the software platform of PPCR, and combines statistics clustering with BP artificial nerve net by the means of the mode editor function It is completely practical and very applied. The third is that we observed every model in the second and third kind of body and discovered that the parent population distribution of similar characters had been separated into the same model impersonally and that many samples that were explained as the same micrafacies always come from the neighbouring sects of the same well. That means the indicated granularity matrix has the capacity of identifying the models for the same sort of samples. The fourth is that compared the granularity parent population distributional model with the transcendent sedimentary environment of building samples ,we discovered that most models of transcendent sedimentary environment fell on together, with great sample's probability, several samples with the similar names or geographical position. That means the indicated granularity matrix have the very good function of identifying facies. The fifth is that we set up a colossal and complicated granularity parent population-sedimentary environment knowledge storeroom through this research. Computers can complete the work of identifying models by emulational man-brainpower"artificial brainpower". It is the most important character in this research that the man-impossible work of identifying model can be completed in a very moment.In conclusion, this paper identifies the sedimentary environment quickly through the results of granularity analysis, which has the importantly applied value for researches on the sedimentary facies of reservoir in Beier depression of Hailaer basin and meanwhile has the referenced value for researching on the others basins in the future.