Daily production prediction of oil wells based on machine learning

Xiao-yan Wang; Yan-juan Ma; Er-zhan Fei; Yuan-fei Gao

doi:10.1117/12.2686768

10 August 2023 Daily production prediction of oil wells based on machine learning

Xiao-yan Wang, Yan-juan Ma, Er-zhan Fei, Yuan-fei Gao

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 1275923 (2023) https://doi.org/10.1117/12.2686768
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

As a non-renewable fossil energy source, oil is closely related to the development of the national economy. The output of oil is equivalent to the blood of the national economy, so reasonable prediction and planning of the output of fossil energy are particularly important. Based on the 10-year daily oil production data of 62 oil wells, this paper comprehensively analyzed some main factors affecting oil production. Through data preprocessing and eigenvalue correlation research and analysis, 13 different characteristic attributes of oil wells were selected. The data were grouped and ungrouped by blocks, respectively. The polynomial regression algorithm model and the random forest model were developed and trained for the two kinds of data. The prediction results of different models were compared, and the models with the smallest error were selected as the quartic polynomial regression model and the random forest model. The prediction of the daily production of the oil well satisfied the requirement of accuracy.

Citation Download Citation

Xiao-yan Wang, Yan-juan Ma, Er-zhan Fei, and Yuan-fei Gao "Daily production prediction of oil wells based on machine learning", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 1275923 (10 August 2023); https://doi.org/10.1117/12.2686768

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