A comparative study of deep learning methods for drilling performance prediction

Tong Jiao; Ye Liu; Jie Cao; Dan Sui

doi:10.1117/12.2674979

23 May 2023 A comparative study of deep learning methods for drilling performance prediction

Tong Jiao, Ye Liu, Jie Cao, Dan Sui

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 126044A (2023) https://doi.org/10.1117/12.2674979
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

Rate of penetration is a key parameter to describe the efficiency of drilling through the formations and it has always been a measure of drilling efficiency. Previous research has demonstrated that machine learning can be applied to improve the prediction of ROP from conventional correlation approaches. More recently, deep learning models have also been used toward that purpose. In this research, the typical used machine learning methods and deep learning methods are tested and compared in terms of ROP prediction. An open-source drilling dataset is used for single well and multiple well modeling and testing. The results demonstrate that deep learning models have more general and accurate results, and LSTM shows solid prediction performance for both single well and multiple well cases. The accurate prediction model for ROP can be further applied for the planning phase and real-time operation optimization.

Citation Download Citation

Tong Jiao, Ye Liu, Jie Cao, and Dan Sui "A comparative study of deep learning methods for drilling performance prediction", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 126044A (23 May 2023); https://doi.org/10.1117/12.2674979

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