Prediction of Coalbed Methane Production Based on BP Neural Network

¹ PETROCHINA CBM Institute Of Engineering Technology, Xi’an, Shanxi, 710082, China
² School of Sciences, Southwest Petroleum University, Chengdu, Sichuan, 610500, China
³ State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, Sichuan, 610500, China

^* Corresponding Author: Yifang Tang; email: tangyifang_swpu@163.com; phone:15828532038

Abstract

The low average daily gas production per well and the poor economic benefit of exploration and development have become the main problems restricting the exploration and development of coalbed methane in China. Combining multiple coal seam geological parameters to predict the high-yield area of the block can not only provide guidance for the exploitation of coal-bed methane, but also bring enormous economic benefits. Aiming at the difficulty of coalbed methane dessert discrimination and production prediction, a method of coal-bed methane production prediction based on BP neural network is proposed in this paper. Starting from the average daily production of coalbed methane single well, we use the method of grey correlation degree to get the main controlling factors of coalbed methane production. For the main control factors, we use BP neural network with high fitting accuracy and get a good prediction result.