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.

In order to further understand the complex spatial distribution caused by the extremely strong heterogeneity of buried hill reservoirs, this paper proposes a new method for predicting the development pattern of buried hill reservoirs based on the traditional pre-drilling prediction and post-drilling evaluation methods that mainly rely on seismic, logging, and core data, which are difficult to meet the timeliness and accuracy of drilling operations. Firstly, the box method and normalization formula are used to process and normalize the abnormal data of element logging and engineering logging, and then the stepwise regression analysis method is used to optimize the sensitive parameters of element logging and engineering logging. The Light Gradient Boosting Machine (LightGBM) algorithm, deep neural network (DNN), and support vector machine (SVM) are used to establish a new method for predicting the development pattern of buried hill reservoirs. Lastly, a comprehensive evaluation index F1 score for the model is established to evaluate the prediction model for the development pattern of buried hill reservoirs. The F1 score value obtained from this model’s comprehensive evaluation index indicates that the LightGBM model achieves the highest accuracy, with 96.7% accuracy in identifying weathered zones and 95.8% accuracy in identifying interior zones. The practical application demonstrates that this method can rapidly and accurately predict the development mode of buried hill reservoirs while providing a new approach for efficient on-site exploration and decision-making in oil and gas field developments. Consequently, it effectively promotes exploration activities as well as enhances the overall process of oil and gas reservoir exploration.