Research on the Application of Integrated Learning Models in Oilfield Production Forecasting.

Abstract

Forecasting oil production is crucially important in oilfield management. Currently, multifeature-based modeling methods are widely used, but such modeling methods are not universally applicable due to the different actual conditions of oilfields in different places. In this paper, a time series forecasting method based on an integrated learning model is proposed, which combines the advantages of linearity and nonlinearity and is only concerned with the internal characteristics of the production curve itself, without considering other factors. The method includes processing the production history data using singular spectrum analysis, training the autoregressive integrated moving average model and Prophet, training the wavelet neural network, and forecasting oil production. The method is validated using historical production data from the J oilfield in China from 2011 to 2021, and compared with single models, Arps model, and mainstream time series forecasting models. The results show that in the early prediction, the difference in prediction error between the integrated learning model and other models is not obvious, but in the late prediction, the integrated model still predicts stably and the other models compared with it will show more obvious fluctuations. Therefore, the model in this article can make stable and accurate predictions.