The impact of step sizes on pressure prediction in fracturing treatment via deep learning algorithms-Reference-Cited by-同舟云学术

The impact of step sizes on pressure prediction in fracturing treatment via deep learning algorithms

Published:2024-08-19 Issue: Volume: Page:1-17
ISSN:1091-6466
Container-title:Petroleum Science and Technology
language:en
Short-container-title:Petroleum Science and Technology

Author:

Liu Yuxuan¹²^ORCID,Peng Ziyi¹,Liu Zhong-Hui¹,Wu Liansong²,Wu Yutong¹,Guo Jianchun²^ORCID

Affiliation:

1. School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu, China

2. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/10916466.2024.2391464

Reference37 articles.

1. Time series predicting of COVID-19 based on deep learning

2. Integrated analysis of pressure response using pressure-rate convolution and deconvolution techniques for varied flow rate production in fractured formations

3. Ben, Y., M. Perrotte, M. Ezzatabadipour, I. Ali, S. Sankaran, C. Harlin, and D. Cao. 2020. Real-time hydraulic fracturing pressure prediction with machine learning. Paper presented at the SPE Hydraulic Fracturing Technology Conference and Exhibition, The Woodlands, Texas, USA. doi:10.2118/199699-MS.

4. Creps Merl J.2018. A Supervised Machine Learning Approach Using Object-Oriented Programming Principles. The University of Toledo MAI 58/03M(E) Masters Abstracts International .

5. Di Vaira, N. J., Ł. Łaniewski-Wołłk, R. L. Johnson, Jr., S. M. Aminossadati, and C. R. Leonardi. 2021. A novel methodology for predicting micro-proppant screenout in hydraulic fracturing treatments. Paper presented at the Asia Pacific Unconventional Resources Technology Conference 2021. doi:10.15530/AP-URTEC-2021-208342.