Fusion attention mechanism-based bidirectional long short term memory for carbonate gas well productivity prediction

Weijun Ni; Hong Xu; Gang Liu; Fei Ma; Chaobo Fan; Cong Xie

doi:10.1117/12.3011562

7 December 2023 Fusion attention mechanism-based bidirectional long short term memory for carbonate gas well productivity prediction

Weijun Ni, Hong Xu, Gang Liu, Fei Ma, Chaobo Fan, Cong Xie

Author Affiliations +

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129413L (2023) https://doi.org/10.1117/12.3011562
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

Our proposed approach for predicting the production capacity of gas wells in carbonate reservoirs involves the utilization of a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network integrated with a fusion attention mechanism, addressing the significant errors in traditional empirical formula and mathematical analytical methods for carbonate reservoir gas well production prediction. By utilizing deep learning methods, we can accurately capture the complex relationships and nonlinear patterns among a large volume of data and extract valuable information from extensive datasets, enabling precise predictions of carbonate reservoir gas well production capacity. The incorporation of the attention mechanism into the Bi-LSTM network architecture enhances not only the ability to handle input subsets but also the capability to process time series data. We apply this model to predict the daily gas production of carbonate reservoir gas wells, and the results demonstrate superior prediction performance compared to individual Bi-LSTM and LSTM models.

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

Citation Download Citation

Weijun Ni, Hong Xu, Gang Liu, Fei Ma, Chaobo Fan, and Cong Xie "Fusion attention mechanism-based bidirectional long short term memory for carbonate gas well productivity prediction", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129413L (7 December 2023); https://doi.org/10.1117/12.3011562

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
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Carbonates

Data modeling

Machine learning

Analytic models

Performance modeling

Data processing

Neural networks

Keywords/Phrases

Search In:

Publication Years