An Artificial Intelligence Prediction Method of Bottomhole Flowing Pressure for Gas Wells Based on Support Vector Machine

The flowing bottomhole pressure (FBHP) of gas wells was affected by many factors. Although a lot of research works have been done to predict the FBHP and at least more than ten models were proposed, but no one can effectively provide an accurate results for all ranges of production data and conditions due to the existence of many uncertain relations between the changeable influence factors. In this paper, an artificial intelligence prediction method for FBHP based on the support vector machine (SVM), named the FBHP-SVM method, was studied, and a support vector regression (SVR) model with epsilon-insensitive loss function (epsilon-SVR) based on radial basis function (RBF) was used to predict the FBHP of gas wells. Compared with the true values, the average absolute and relative errors of the new method were 0.27MPa and 2.29%, respectively. The FBHP-SVM method was also compared to the vertical pipe flowing method. The results showed this new method was a new practical tool to predict FBHP in gas wells and it had a satisfying prediction accuracy.