Constructing the prediction model for oil well wax deposition and its application

Tianshi Liu; Minjie Hao; Tao Deng; Yingna Duan; Zehua Zhou; Jiachen Liu; Jianlin Jiang

doi:10.1117/12.3009559

16 October 2023 Constructing the prediction model for oil well wax deposition and its application

Tianshi Liu, Minjie Hao, Tao Deng, Yingna Duan, Zehua Zhou, Jiachen Liu, Jianlin Jiang

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128031A (2023) https://doi.org/10.1117/12.3009559
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

In the process of oil field production, wax deposition in oil well will lead to lower pumping efficiency and even wax sticking in well. In response to the problem of low accuracy in determining the flushing cycle of oil well wax removal and prevention work relying on manual experience, the degree of wax deposition in the oil well is obtained by analyzing the indicator diagram of the oil well working conditions and using the load difference. The Pearson correlation coefficient is used to analyze the correlation of oil well parameters and establish a dataset of oil well load. On this basis, the oil well wax deposition prediction model is constructed by using the Long Short-Term Memory network, and the optimal parameters matching the network structure are searched by Particle Swarm Optimization algorithm. The prediction model is utilized to train the sample dataset, obtain the prediction data and realize the pre-warning of oil well wax deposition. Finally, the least square curve fitting method is used to obtain the growth rate of load difference and the wax deposition state of the well. The experimental results indicate that the constructed oil well wax deposition prediction model can obtain the wax deposition status in advance, guide the wax removal and prevention work, and provide decision support for the formulation of the well flushing cycle in the oil field.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tianshi Liu, Minjie Hao, Tao Deng, Yingna Duan, Zehua Zhou, Jiachen Liu, and Jianlin Jiang "Constructing the prediction model for oil well wax deposition and its application", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128031A (16 October 2023); https://doi.org/10.1117/12.3009559

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Particle swarm optimization

Education and training

Statistical modeling

Correlation coefficients

Machine learning

Particles

Show All Keywords

Keywords/Phrases

Search In:

Publication Years