Research On Logging Evaluation Method Of Glutenite Reservoir In FC Oilfield

The abundance of glutenite reservoir in China ensures the prospects and value of its recovery and development. Glutenite reservoir, as a result, is one of the key directions in the field of oil and gas exploration and development. However, glutenite has characteristics such as strong vertical heterogeneity, changing lithofacies, and uneven reservoir layers, which makes well logging rather challenging. The regular well logging and evaluation techniques are of poor applicability in glutenite reservoir due to the errors that might occur in rock property recognition, physical data calculation and fluid property recognition, which may, as a result, affect the following geological modeling, volume calculation and exploitation plan development. Therefore, the study of well logging techniques in glutenite reservoir is of great value.This paper is based on the glutenite reservoir in FC field with reference to the core analysis, well logging data and oil test data. Firstly, a study of features of the reservoir layers and relations of the four properties was conducted. An analysis of the reservoir's physical features, lithologic property and oil property, including the microscopic features such as rock formation and particle type, was conducted to find out the relation of the glutenite reservoir's four properties and the differences of the physical property, electric property and oil property of different lithologies.Secondly, this paper included the study of logging reaction of different lithologies, which supported a further study of lithology recognition methods using crossplots, discriminant analysis and BP neural network. Furthermore, a quality assessment and comparison of different methods was introduced to select the one that collected the most accurate data.Thirdly, a following study of data calculation methods regarding the saturation data and physical data of the reservoir was conducted in this paper. The experience formula was used to find the shale content; the statistic formula and BP network was used to calculate the porosity and permeability; the Archie equation and BP neural network were used to determine the saturation. In order to find the most suitable method, a quality assessment and comparison of the methods in question was included.At last, several methods were used to determine the reservoir's saturation lower bound and physical properties lower bound, by doing which a set of reservoir lower limit and a well logging interpretation procedure could be formed. By incorporating those methods with production data and oil test data, the accuracy of the interpretation of the oil layer and water layer tested in 16 wells of study area was 94%.