Identification of CO2 using multiple formation properties based on nuclear logging

•A new method for effectively identifying CO2 from water, oil, and hydrocarbon gases is proposed.•The slopes between different formation parameters are used to amplify the difference between CO2 and hydrocarbon gases.•The CO2 identification method is less affected by environmental factors such as porosity, lithology, and shale content. The use of carbon dioxide to enhance oil and gas recovery (CO2-EOR) plays an important role in carbon capture, utilization, and storage (CCUS). CO2 monitoring is of great significance for understanding CO2 migration mechanisms, leakage pathways, and storage effect, and it is help for optimizing the performance of CO2-EOR and risk assessment. Moreover, oil and gas fields involving CO2 also need to monitor and identify CO2 layers to support decisions on oil and gas exploration and development. The purpose of this study is to use nuclear logging methods to distinguish CO2 from other fluids, including hydrocarbon gases, and to monitor and identify CO2. Based on the differences in the formation properties of CO2 and other fluids, a slope method is proposed. The slopes related to different formation properties are calculated by the Monte Carlo method. The sensitivity of various slopes in distinguishing CO2 from hydrocarbon gases (hereinafter referred to as “sensitivity”) is analyzed. The cross-plot of slopes with high sensitivity is used to identify CO2 from other fluids. The effects of various environmental factors on the cross-plot and the applicability of the cross-plot method are analyzed. The results indicate that the positions of CO2 points in the cross-plot are greatly different from the positions of water, oil, and CH4, and the cross-plot can effectively identify CO2 from other fluids. Lithology and shale content have little effect on the performance of the cross-plot in identifying CO2 from other fluids. Comparatively, gas density and water saturation have greater impacts on such performance. The results of interpretation of field data obtained with the new method are basically consistent with the sample results, indicating that the new method is effective. This method can provide strong support for making decision on the exploration and development of oil and gas fields and has promising prospects for application in CO2 monitoring in CO2-EOR.