Open AccessCalculation method of production pressure drop based on BP neural network velocity pipe string production in CBM wells
Author(s) -
Hongtu Zhu,
Yaoguang Qi,
Fenna Zhang,
YunfeiSong,
LiangLin,
Jian Zhang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/619/1/012044
Subject(s) - pressure drop , petroleum engineering , drop (telecommunication) , artificial neural network , drainage , coalbed methane , engineering , string (physics) , nonlinear system , mechanics , mechanical engineering , computer science , mathematics , coal mining , artificial intelligence , physics , coal , ecology , quantum mechanics , mathematical physics , biology , waste management
In the stable production stage CBM wells have the characteristics of high gas production and low water production. The use of continuous velocity tube technology for drainage can achieve better drainage results. Accurate and rapid prediction of the pressure drop of velocity pipe string production in a coalbed methane well has become the key to the operation and management of velocity pipe technology. This paper uses the nonlinear mapping and prediction capabilities of the BP neural network to build a three-layer BP neural network to construct a velocity pipe string production pressure drop prediction model. The model is based on gas production, water production, bottom hole pressure, pipe string diameter, and well depth. The five factors are input, and the pressure drop of the pipe string is the output, which can quickly and accurately realize the pressure drop analysis and calculation of the speed pipe drainage. The analysis shows that it is feasible to use the BP neural network to calculate and analyse the pressure drop of the velocity string of coalbed methane wells.