Predicting the oil production using the novel multivariate nonlinear model based on Arps decline model and kernel method

Prediction of petroleum production plays a key role in the petroleum engineering, but an accurate prediction is difficult to achieve due to the complex underground conditions. In this paper, we employ the kernel method to extend the Arps decline model into a nonlinear multivariate prediction model, which is called the nonlinear extension of Arps decline model (NEA). The basic structure of the NEA is developed from the Arps exponential decline equation, and the kernel method is employed to build a nonlinear combination of the input series. Thus, the NEA is efficient to deal with the nonlinear relationship between the input series and the petroleum production with a one-step linear recursion, which combines the merits of commonly used decline curve methods and intelligent methods. The case studies are carried out with the production data from two real-world oil field in China and India to assess the efficiency of the NEA model, and the results show that the NEA is eligible to describe the nonlinear relationship between the influence factors and the oil production, and it is applicable to make accurate forecasts for the oil production in the real applications.