While drilling formation pressure monitoring is an important basis for ensuring drilling safety and oil and gas discovery, the calculation of existing pressure monitoring methods is complicated and the accuracy is difficult to improve. Taking the actual well data of well area X in Yinggehai Basin as the object, correlation analysis was first carried out to select and standardize the data features, and relevant effective parameters were extracted. Two kinds of neural networks, back-propagation network BP and back-propagation network GA-BP optimized by genetic algorithm, were used to establish artificial intelligence monitoring models of formation pressure based on 10 kinds of measuring and logging data, respectively. The application effect of the model was evaluated based on the results of monitoring the pressure while drilling. The results show that the monitoring accuracy of the BP neural network model is 91.25%, and that of the GA-BP neural network model is 92.89%. The latter has a better monitoring effect on formation pore pressure. In formation pressure monitoring in areas with a high degree of well control, the introduction of artificial intelligence technology has the advantages of simplicity, speed and high precision, and can provide a reference for other areas of pressure monitoring while drilling.

Rock grain size parameter is the key parameter of reservoir rock physics analysis. The study found that the relationship between the NMRspectrum and rock grain size distribution curve is directly related to NMRdistribution and grain size distribution of rock, so you can use to retrieve the size distribution of rock NMR spectral data. Based on the calculation of the rock core experiment results of grain size distribution by NMR distribution using the conversion method of piecewise nonlinear calibration for each core, adjusting the calibration parameters to make the grain size distribution calculated with the core analysis of grain size distribution approximation, the error is minimum to obtain each core scale parameter conversion. In the end, the core parameters are classified according to the parameters of pore and permeability. Finally, according to the actual NMR distribution curve inversion of underground rock granularity, the inversion results contrast with the core analysis results, and the reliability of the method is verified to provide accurate, continuous rock size distribution profile analysis of geological reservoir rock physics.

Carbon isotope logging technology can obtain timely and accurate hydrocarbon fluid and reservoir geological information and has great application potential in oil–gas body property analysis, the comprehensive study of source rocks, and fault-sealing evaluation. Since 2014, real-time methane isotope logging technology has been applied in the western South China Sea. Based on the on-site, real-time, continuous, and accurate detection of methane carbon isotopes, combined with the rapid comprehensive analysis and evaluation of logging gas measurement data, the application effect in ultra-high-temperature and high-pressure wells in the western South China Sea has been remarkable. Taking the Ledong 10 Area of Yinggehai Basin as an example, real-time carbon isotope logging data can be used to quickly identify gas origins, source rock maturity, and gas source type and help judge the sealing quality of overburdened mudstone caps. This knowledge can serve as a reference for ascertaining the popularity of isotope-logging technology in other areas.