Data-driven approaches for time series prediction of daily production in the Sulige tight gas field, China

Abstract

The Sulige tight gas field is presently the largest gas field in China. Owing to the ultralow permeability and strong heterogeneity of the reservoirs in Sulige, the number of production wells has exceeded 3,000, keeping the stable gas supply in the decade. Thus, the daily production prediction of gas wells is significant for monitoring production and for implementing and evaluating stimulation measures. Therefore, on the basis of the three data-driven time series approaches, the daily production of 1692 wells over 10 years was mining for the daily production prediction of wells in Sulige. The jointed deep long short-term memory and fully connected neural network (DLSTM-FNN) model was proposed by introducing the recurrent neural network's sequential expression ability and was compared with random forest (RF) and support vector regression (SVR). After the daily production predictions of thousands of wells in Sulige, the proposed DLSTM-FNN model significantly improved the time series prediction accuracy and efficiency in the short training samples and had strong availability and practicability in the Sulige tight gas field.