The effective measuring range of an electromagnetic wave resistivity instrument used in logging while drilling (LWD) is small, and the resistivity measurement is greatly influenced by the dielectric constant, especially in high-dielectric-constant formations. In this paper, the response characteristics of the instrument under a high dielectric constant are investigated by a numerical simulation algorithm, and the resistivity conversion method is determined. The results show that the higher the working frequency of the electromagnetic wave resistivity instrument while drilling, and the greater the formation of background resistivity, the greater the influence of the dielectric constant on the logging response. The existence of the dielectric constant will cause the phase shift and amplitude attenuation of the measured signal to migrate, and this migration is proportional to the formation resistivity and the dielectric constant. According to this rule, the resistivity–permittivity response library is established, and the formation permittivity is calculated by the inversion of the library. On the basis of obtaining the formation permittivity, the migration of the logging signal permittivity is corrected, the influence of the dielectric constant is eliminated, and the measuring precision and measuring range of the instrument in the high-dielectric-constant formation are enlarged.

Tourism geographical information syst_ em (TGIS), which needs to integrate, manage, publish a lot of spatial and attribute data on the Internet, and ArcIMS provides us with an ideal technical platform. This paper analyzed the system

Shale oil reservoirs are characterized by complex lithology, complex mineral composition and strong heterogeneity. This causes great difficulty in lithologic evaluation. In this paper, a method of lithology identification is proposed by means of intersection plot method and machine learning method, and lithology evaluation is carried out by combining the calculation of mineral content with a multi-mineral optimization model. The logging response characteristics of five lithologies are analyzed by using the logging curves selected by principal component analysis (PCA) discriminant analysis. In lithology identification, the system clustering algorithm is selected to identify shale oil reservoir lithology through layer-by-layer subdivision of sample lithology classification. Logging data has high vertical resolution and good continuity, and mineral prediction using logging data can ensure high accuracy. In this paper, the method of calculating mineral content by using multi-mineral optimization model has achieved good results in practice.

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