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Shale gas wells in the Changning block of Weiyuan, Sichuan, China, experience water production problems throughout their life cycles. In actual field production, plunger lift is typically used to discharge liquid from a shale gas well. However, it is difficult to test the formation inflow performance relationship curve (IPR) of a plunger lift well. Hence, data on the formulation and optimization of plunger lift well systems are lacking. Based on actual measured data related to casing pressure changes at the wellhead and the gas-liquid distribution characteristics in a wellbore during the shut-in phase, this paper establishes a method for calculating the formation IPR curve of a shale gas plunger lift well by calculating changes in wellbore gas volume continuously. This method has a fast calculation speed. The gas-water interface distribution can be obtained in 3–5 iterations, and the inflow dynamic curve can be calculated in less than 5 seconds. This method tested the IPR curves of gas wells in the Changning block. The IPR curves of plunger lift wells showed concave characteristics contrary to the conventional upward convex curves. This is because the bottom hole flowing pressure changes caused variations in the water saturation of the near-wellbore zone. Consequently, the productivity equation of the plunger lift well is established by introducing the variation coefficient of water saturation expressed by bottom hole flow pressure. This equation fits with the measured IPR curve with an average R2 of approximately 0.9989. [ABSTRACT FROM AUTHOR]

Effectively predicting the spatial distribution of oil and gas contributes to delineating promising target areas for further exploration. Determining the location of hydrocarbon is a complex and uncertain decision problem. This paper proposes a method for predicting the spatial distribution of oil and gas resource based on Bayesian network. In this method, qualitative dependency relationship between the hydrocarbon occurrence and key geologic factors is obtained using Bayesian network structure learning by integrating the available geoscience information and the current exploration results and then using Bayesian network topology structure to predict the probability of hydrocarbon occurrence in the undiscovered area; finally, the probability map of hydrocarbon-bearing is formed by interpolation method. The proposed method and workflow are further illustrated using an example from the Carboniferous Huanglong Formation (C2hl) in the eastern part of the Sichuan Basin in China. The prediction results show that the coincidence rate between the results of 248 known exploration wells and the predicted results reaches 89.5%, and it has been found that the gas fields are basically located in the high value area of the hydrocarbon-bearing probability map. The application results show that the Bayesian network method can effectively predict the spatial distribution of oil and gas resources, thereby reducing exploration risks, optimizing exploration targets, and improving exploration benefits. [ABSTRACT FROM AUTHOR]