TY  - CONF
EP  - 102
A1  - Kumar, A.
A1  - Ridha, S.
A1  - Ilyas, S.U.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097532657&doi=10.1109%2fICCI51257.2020.9247667&partnerID=40&md5=e36cbbf912af01597fca1517fa1c4306
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781728154473
Y1  - 2020///
TI  - Unsupervised Deep Learning Algorithm to Solve Sub-Surface Dynamics for Petroleum Engineering Applications
ID  - scholars12639
SP  - 98
KW  - Boundary value problems; Complex networks; Flow of fluids; Intelligent computing; Learning algorithms; Learning systems; Numerical methods; Ordinary differential equations; Partial differential equations; Petroleum engineering; Petroleum reservoirs
KW  -  Drilling optimization; Engineering applications; High-dimensional problems; Mathematical problems; Numerical techniques; Ordinary and partial differential equations; Reservoir simulation; Universal approximation
KW  -  Deep learning
N1  - cited By 0; Conference of 2020 International Conference on Computational Intelligence, ICCI 2020 ; Conference Date: 8 October 2020 Through 9 October 2020; Conference Code:164916
N2  - Ordinary and partial differential equations play a significant role across various energy domain as they aid in approximating solution for complex mathematical problems. Drilling optimization and reservoir simulation are some common application that takes the form of differential equations and are dominated by their respective governing equations. Approximating the solution of such mathematical problems requires a fast and reliable methodology. However, the computational complexity increases with the dimension for the classical numerical techniques and the quality of the result is dependent upon the discretization and sampling methods of the subspace. Recent advances in deep learning techniques, based on universal approximation theorem of neural network seems promising to tackle the high dimensional problem. The solution provided by deep learning for a differential equation is in a closed analytical form which is differentiable and could be used in any subsequent computation. In the present study, the solution for the initial condition and boundary value problems in ordinary and partial differential equation by deep learning method have been analyzed. The propsed algorithm could be valuable aid for analyzing the fluid flow and reservoir simulation in an effective manner. © 2020 IEEE.
AV  - none
ER  -