Jialingjiang Formation is a typical low permeability carbonate reservoir in Sichuan Basin, which is characterized by strong heterogeneity, low matrix permeability and high-water saturation. Based on seven influencing factors, such as permeability, reservoir thickness and reservoir porosity, this paper evaluates and predicts the oil recovery of Jialingjiang Formation gas reservoir by using two neural network models of multilayer perceptor and radial basis function and nonlinear surface fitting

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... thod. The results show that: 1) In the neural network prediction model, the correlation coefficient between the prediction result curve of multilayer perceptron and the original recovery curve reaches 0.88, which is higher than the radial basis function (0.81), indicating that the use of multilayer perceptron can better predict the recovery of gas reservoirs. 2) In the nonlinear curved surface fitting prediction model, the two influencing factors with the greatest linear correlation with the recovery factor, reservoir thickness and gas recovery rate, are selected to fit the prediction model, and the prediction model is obtained. According to the prediction model, the recovery factor is positively correlated with the reservoir thickness and gas recovery rate on the whole. The model can be used to estimate gas recovery from two factors: reservoir thickness and gas recovery rate.