scholarly journals From SIR to SEAIRD: A novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19

Author(s):  
K. Midzodzi Pekpe ◽  
Djamel Zitouni ◽  
Gilles Gasso ◽  
Wajdi Dhifli ◽  
Benjamin C. Guinhouya
Author(s):  
Shams Kalam ◽  
Rizwan Ahmed Khan ◽  
Shahnawaz Khan ◽  
Muhammad Faizan ◽  
Muhammad Amin ◽  
...  

2017 ◽  
Vol 13 (7) ◽  
pp. e1005627 ◽  
Author(s):  
Warren D. Anderson ◽  
Danielle DeCicco ◽  
James S. Schwaber ◽  
Rajanikanth Vadigepalli

AIAA Journal ◽  
2021 ◽  
pp. 1-13
Author(s):  
Sean T. Kelly ◽  
Andrea Lupini ◽  
Bogdan I. Epureanu

2013 ◽  
Author(s):  
Peter John Dzurman ◽  
Juliana Yuk Wing Leung ◽  
Stefan David Joseph Zanon ◽  
Ehsan Amirian

2020 ◽  
Vol 38 (6) ◽  
pp. 2413-2435 ◽  
Author(s):  
Xinwei Xiong ◽  
Kyung Jae Lee

Secondary recovery methods such as waterflooding are often applied to depleted reservoirs for enhancing oil and gas production. Given that a large number of discretized elements are required in the numerical simulations of heterogeneous reservoirs, it is not feasible to run multiple full-physics simulations. In this regard, we propose a data-driven modeling approach to efficiently predict the hydrocarbon production and greatly reduce the computational and observation cost in such problems. We predict the fluid productions as a function of heterogeneity and injection well placement by applying artificial neural network with small number of training dataset, which are obtained with full-physics simulation models. To improve the accuracy of predictions, we utilize well data at producer and injector to achieve economic and efficient prediction without requiring any geological information on reservoir. The suggested artificial neural network modeling approach only utilizing well data enables the efficient decision making with reduced computational and observation cost.


Sign in / Sign up

Export Citation Format

Share Document