Open AccessIdentification of Indicator Diagram Type in the Oil Well by BP Neural Network
Author(s) -
Jianxun Jiang,
Xiaofan Li
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/781/2/022057
Subject(s) - diagram , artificial neural network , petroleum engineering , identification (biology) , wax , type (biology) , oil well , computer science , data mining , biological system , artificial intelligence , engineering , geology , chemistry , paleontology , botany , organic chemistry , database , biology
Wax sticking in oil wells has always been a difficult problem in oil exploitation. Wax sticking in oil wells exists not only in the exploitation stage, but also in every link of oil production. Accurate identification of indicator diagram type is very important to prevent oil well wax sticking. In this paper, a BP neural network method is proposed to identify indicator diagram types. This model makes full use of indicator diagram data, simplifies complex mechanism research, and has wider practicability. Through the calculation of an example, the BP neural network established in this paper can accurately identify the type of indicator diagram.