Application on crude oil output forecasting based on TB-SCM algorithm

Factors that affect crude oil output are multifarious and non-linear, so it is very difficult to analyze and predict the crude oil output solely based on mathematical methods. This paper presents a new method that applies TB-SCM algorithm to predict crude oil output. Firstly, the monthly production data of the past years from a sample oil plant is preprocessed by the K-means algorithm, and the transaction dataset is obtained. Next, based on the TB-SCM algorithm, the strong association rules about crude oil output are generated with the given minimum support threshold and minimum confidence threshold. Lastly, these strong association rules can help us to forecast crude oil output in the coming months for oil production plant. Comparing with the actual value of crude oil output, the result shows that the prediction method is of high operational efficiency, simple and accurate.