Comparisons of Oil Production Predicting Models

Yishen Chen, Xianfeng Ding, Haohan Liu, Yongqin Yan

School of Graduate of Southwest Petroleum University, Chengdu, China;China Petroleum Engineering Construction Corporation, Beijing, China.
School of Graduate of Southwest Petroleum University, Chengdu, China;Sichuan College of Architectural Technology, Deyang, China.
School of Petroleum Engineering Management of Southwest Petroleum University, Chengdu, China.
School of Science of Southwest Petroleum University, Chengdu, China.

DOI: 10.4236/eng.2013.58076                

Abstract

Feasibility of oil production predicting results influences the annual planning and long-term field development plan of oil field, so the selection of predicting models plays a core role. In this paper, three different predicting models are introduced, they are neural network model, support vector machine model and GM (1, 1) model. By using these three different models to predict the oil production in XINJIANG oilfield in China, advantages and disadvantages of these models have been discussed. The predicting results show: the fitting accuracy by the neural network model or by the support vector machine model is higher than GM (1, 1) model, the prediction error is smaller than 10%, so neural network model and support vector machine model can be used to short-term forecast of oil production; predicting accuracy by GM (1, 1) model is not good, but the curve trend with GM (1, 1) model is consistent with the downward trend in oil production, so GM (1, 1) predicting model can be used to long-term prediction of oil production.

Keywords

Oil Field; Oil Production; Model; Predicting Accuracy

Conflicts of Interest

The authors declare no conflicts of interest.

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