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.

The current gas logging electric degasser is susceptible to various factors, such as drilling fluid displacement, drilling fluid level height, motor speed, and drilling fluid properties. Consequently, the degassing efficiency varies, and quantification is poor. The gas chromatographic analysis time is lengthy, the gas path structure is complex, and the operation process is intricate. These factors distort gas measurement data and compromise the accuracy of hydrocarbon discovery and interpretation. To address these issues associated with traditional logging equipment, a novel gas logging instrument based on semipermeable film degasser and infrared spectroscopy has been developed. This instrument utilizes the principle of quantitative separation of gas and liquid through a semipermeable membrane combined with rapid and highly sensitive infrared spectroscopic analysis for precise gas characterization. Laboratory and field tests demonstrate excellent repeatability and stability of this instrument while meeting the detection range requirements for alkane gas concentration in oil fields. Comparison between the hydrocarbon curves obtained from both electric degasser and membrane degassing reveals similar trends; however, the latter provides more comprehensive information.

Overpressure is widely developed in the Enping Formation of the Baiyun Sag, Pearl River Mouth Basin, yet its origin has long remained controversial. Previous studies, based on the acoustic velocity-effective stress crossplot, concluded that undercompaction is the dominant overpressure mechanism in this area; however, the significant influence of high total organic carbon (TOC) in the thick mudstone intervals on sonic transit time was not effectively eliminated. In this paper, geochemical logging data are used to perform TOC correction on the sonic transit time of Well BY-X1. Combined with log-curve assemblages and the effective-stress crossplot, the overpressure origin is re-identified, and the development characteristics and quantitative patterns of overpressure are clarified. The results show that the maximum TOC of mudstones in the overpressure intervals of the Enping Formation in the Baiyun Sag reaches 5.8%, exhibiting a strong positive correlation with sonic transit time. After TOC correction, the reduction in sonic transit time ranges from 6.8% to 29.8%, with an average reduction of 16.8% in the lower Enping Formation. Before correction, data points fall within the loading curve region, leading to a potential misinterpretation of undercompaction as the dominant mechanism; after correction, all data points plot along the unloading curve. Combined with the absence of significant shifts in density logs, the maximum formation pressure coefficient of 1.53, and the lack of anomalously high porosity, it is confirmed that the dominant origin of overpressure in this area is fluid expansion driven by hydrocarbon generation, rather than undercompaction. The sonic transit-time correction method for organic-rich mudstones established in this study can significantly improve the accuracy of formation-pressure prediction, providing a quantitative reference for the study of overpressure mechanisms in source-rock systems with high heat flow and hydrocarbon-rich sags.