Application of artificial intelligence in lithology recognition of petroleum logging in low permeability reservoirs

In low permeability reservoirs, the conversion accuracy of the existing petroleum logging lithology identification methodto smallpore capillary pressure curveis not high, resulting in a low rock massidentification accuracy. Therefore, artificial intelligence technology is considered in this study to enhance the accuracy of lithology identification in low permeability reservoirs. Firstly, the radar mapping program is used to predict the position of reservoir oil logging, and then the small pore capillary pressure curve is converted by using the conversion method of piecewise power function scale to obtain the pore characteristics of low-permeability reservoir rocks. On this basis, the crossplot method is used to gather the pore characteristic data in well logging and form a plan, and the response parameters of well logging rock mass are obtained to realize the identification and analysis of lithology. The experimental results show that, compared with the existing identification methods, the accuracy of lithology identification in low-permeability reservoir logging is significantly increased after the application of artificial intelligence technology, and the identification process takes less time, which fully proves that the application of artificial intelligence technology is conducive to improving the performance of lithology identification.