TY - GEN
T1 - Natural Fracture Network Model Using Machine Learning Approach
AU - Merembayev, Timur
AU - Amanbek, Yerlan
N1 - Funding Information:
The authors wish to acknowledge the support of the research grant, no. AP19575428, from the Ministry of Science and Higher Education of the Republic of Kazakhstan. Authors gratefully acknowledge the support of the Nazarbayev University Faculty Development Competitive Research Grant (NUFDCRG), Grant No. 20122022FD4141.
Publisher Copyright:
© 2023, The Author(s).
PY - 2023
Y1 - 2023
N2 - A fracture network model is a powerful tool for characterizing fractured rock systems. In this paper, we present the fracture network model by integrating a machine learning algorithm in two-dimensional setting to predict the natural fracture topology in porous media. We also use a machine learning algorithm to predict the fracture azimuth angle for the natural fault data from Kazakhstan. The results indicate that the fracture network model with LightGBM performs better in designing a fracture network parameter for hidden areas based on data from the known area. In addition, the numerical result of the machine learning algorithm shows a good result for randomly selected data of the fracture azimuth.
AB - A fracture network model is a powerful tool for characterizing fractured rock systems. In this paper, we present the fracture network model by integrating a machine learning algorithm in two-dimensional setting to predict the natural fracture topology in porous media. We also use a machine learning algorithm to predict the fracture azimuth angle for the natural fault data from Kazakhstan. The results indicate that the fracture network model with LightGBM performs better in designing a fracture network parameter for hidden areas based on data from the known area. In addition, the numerical result of the machine learning algorithm shows a good result for randomly selected data of the fracture azimuth.
KW - Fracture characterization
KW - LightGBM
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85165099299&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85165099299&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-37114-1_26
DO - 10.1007/978-3-031-37114-1_26
M3 - Conference contribution
AN - SCOPUS:85165099299
SN - 9783031371134
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 384
EP - 397
BT - Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
A2 - Scorza, Francesco
A2 - Rocha, Ana Maria A. C.
A2 - Garau, Chiara
A2 - Karaca, Yeliz
A2 - Torre, Carmelo M.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Computational Science and Its Applications, ICCSA 2023
Y2 - 3 July 2023 through 6 July 2023
ER -