An Artificial Intelligence Prediction Method of Bottomhole Flowing Pressure for Gas Wells Based on Support Vector Machine
Qin-Feng Di, Wei Chen, Jing-Nan Zhang, Wen-Chang Wang, Hui-Juan Chen
Available Online December 2016.
- https://doi.org/10.2991/mme-16.2017.28How to use a DOI?
- Flowing bottomhole pressure, Support vector machine, Random samples selection, Gas wells.
- 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 -insensitive loss function ( -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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Qin-Feng Di AU - Wei Chen AU - Jing-Nan Zhang AU - Wen-Chang Wang AU - Hui-Juan Chen PY - 2016/12 DA - 2016/12 TI - An Artificial Intelligence Prediction Method of Bottomhole Flowing Pressure for Gas Wells Based on Support Vector Machine BT - 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016) PB - Atlantis Press SP - 206 EP - 214 SN - 2352-5401 UR - https://doi.org/10.2991/mme-16.2017.28 DO - https://doi.org/10.2991/mme-16.2017.28 ID - Di2016/12 ER -