Reservoir Production Prediction Based on Variational Mode Decomposition and Gated Recurrent Unit Networks

Abstract

Fractured-vuggy carbonate reservoirs have complex geological structures including pores, caves and fractures, which causes frequent working system adjustments and makes the production prediction extremely challenging. Currently, the most widely used methods in such prediction are the water drive characteristic curve methods and machine learning based models. However, frequent working system adjustments (such as well shut-in) could make water drive characteristic curve unstable, which provides unsatisfactory accuracy of the prediction model. In this paper, by integrating the variational mode decomposition (VMD) and gated recurrent unit (GRU), a novel machine learning based prediction model termed VMD-GRU is proposed to address the limitations of the water drive characteristic curve methods. The time-series production data are firstly decomposed by using VMD into several sub-series that represent different characteristics of the data. GRU is used to establish autoregression model, which can extract the inner characteristics of each sub-series and make prediction. The final prediction outputs are obtained by aggregating prediction result of each GRU model. The proposed VMD-GRU model is verified with the real-world production data from the Tahe oilfield of China. The experimental results demonstrate that the proposed VMD-GRU model outperforms the existing production prediction models for fractured-vuggy carbonate reservoirs.