TY - GEN
T1 - Internet of things and big data analytics for smart oil field malfunction diagnosis
AU - Xu, Birong
AU - Wang, Weijiang
AU - Wu, Yuyan
AU - Shi, Yueting
AU - Lu, Chang
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/20
Y1 - 2017/10/20
N2 - With the rapid development of information technology and digital communication, the data types are more abundant by integration of various technologies. In this paper, based on the analysis of a large number of historical data of oil and water wells, the changes of some important parameters of the wells can be monitored and then used in the trend prediction and the early warning system. Subsequently, we use 6 Sigma algorithm to process the historical data, and by the big data trend analysis combining with various parameters, we can diagnose six operating conditions, such as sand production, abnormal of moisture content etc. Through experiments, the algorithm is stable and reliable in practical application, and it has great significance to ensure the normal production of oil field and improve the management ability for oil field.
AB - With the rapid development of information technology and digital communication, the data types are more abundant by integration of various technologies. In this paper, based on the analysis of a large number of historical data of oil and water wells, the changes of some important parameters of the wells can be monitored and then used in the trend prediction and the early warning system. Subsequently, we use 6 Sigma algorithm to process the historical data, and by the big data trend analysis combining with various parameters, we can diagnose six operating conditions, such as sand production, abnormal of moisture content etc. Through experiments, the algorithm is stable and reliable in practical application, and it has great significance to ensure the normal production of oil field and improve the management ability for oil field.
KW - 6 sigma
KW - big data
KW - internet of thing
KW - oil field fault diagnosis
KW - warning thresholds scheme
UR - https://www.scopus.com/pages/publications/85040024325
U2 - 10.1109/ICBDA.2017.8078802
DO - 10.1109/ICBDA.2017.8078802
M3 - Conference contribution
AN - SCOPUS:85040024325
T3 - 2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017
SP - 178
EP - 181
BT - 2017 IEEE 2nd International Conference on Big Data Analysis, ICBDA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Big Data Analysis, ICBDA 2017
Y2 - 10 March 2017 through 12 March 2017
ER -