Production Prediction Model of Tight Gas Well Based on Neural Network Driven by Decline Curve and Data

Abstract

The accurate prediction of gas well production is one of the key factors affecting the economical and efficient development of tight gas wells. The traditional oil and gas well production prediction method assumes strict conditions and has a low prediction accuracy in actual field applications. At present, intelligent algorithms based on big data have been applied in oil and gas well production prediction, but there are still some limitations. Only learning from data leads to the poor generalization ability and anti-interference ability of prediction models. To solve this problem, a production prediction method of tight gas wells based on the decline curve and data-driven neural network is established in this paper. Based on the actual production data of fractured horizontal wells in three tight gas reservoirs in the Ordos Basin, the prediction effect of the Arps decline curve model, the SPED decline curve model, the MFF decline curve model, and the combination of the decline curve and data-driven neural network model is compared and analyzed. The results of the case analysis show that the MFF model and the combined data-driven model have the highest accuracy, the average absolute percentage error is 14.11%, and the root-mean-square error is 1.491, which provides a new method for the production prediction of tight gas wells in the Ordos Basin.