Shale gas well flowback rate prediction for Weiyuan field based on a deep learning algorithm

Abstract

The flowback rate of a shale gas well is controlled by many factors, such as geological and engineering factors, which have a certain complexity. For the aforementioned reason, the knowledge-driven method often has difficulty effectively capturing the correlation rules among multiple datasets. As a data-driven algorithm, deep learning has strong advantages in data correlation analysis, nonlinear fitting and other applications. In this paper, an algorithm is set up to forecast the shale gas well flowback rate by using a deep learning algorithm based on the flowback characteristic factors and data from 286 shale gas wells in the Weiyuan field. An algorithm and prediction model of the flowback parameters of the Weiyuan shale gas wells is established to effectively capture their flowback characteristics. First, the general situation of the study area is briefly introduced, and the data used to predict the flowback rate in this area are analyzed. Second, based on the deep learning algorithm, two kinds of deep feedforward neural network structures and a neural network-based prediction method of shale gas well flowback rate are designed. Finally, according to the regional data, the prediction and analysis of the flowback rate of Weiyuan shale gas wells are carried out. The results show that the correlations between 17 factors, such as fracturing section length and shale gas well flowback rate, are not obvious, but that the deep learning method proposed in this paper can effectively and accurately capture the correlations between the data and predict the flowback rate. The results show high accuracy.