Abstract: When using generalized Weng's model to study the prediction of oil production,linear trial and error method is often used to estimate the parameters of the model. This method needs to set one parameter value beforehand and obtain the other two parameters under this condition,which is easy to be influenced by some artificial factors. Based on Maxwell distribution in statistics,a two-parameter Maxwell model was established. Furthermore, three different parameter estimation methods,such as nonlinear optimization,univariate polynomial regression and binary linear regression,were proposed to solve two parameters in the Maxwell model. Taking oil production from 1971 to 2016 in Norway as training data set,the solving processes and calculation results of the three methods were compared,and the advantages and disadvantages of the three methods were analyzed. The results show that the oil production data obtained by the three methods are in good agreement with the actual data, which proves the applicability of the Maxwell model and the accuracy of the parameter estimation method. In addition,the mean square deviations obtained by two regression methods are very small,which shows that the precisions of regression modeling methods are higher,and the maximum production year fitted by non-linear optimization method is more accurate. This study provides a new model and an effective parameter estimation method for oil and gas field production prediction.

Key words: oil production, generalized Weng's model, linear trial and error method, unary polynomial regression, linear regression, parameter estimation