TY  - JOUR
Y1  - 2021///
PB  - Springer Science and Business Media Deutschland GmbH
SN  - 21984182
A1  - Irfan, S.A.
A1  - Shafie, A.
JF  - Studies in Systems, Decision and Control
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093869953&doi=10.1007%2f978-981-15-8606-4_4&partnerID=40&md5=f9d49e75e6f16db1805039f699a618fb
EP  - 75
VL  - 320
AV  - none
N1  - cited By 6
N2  - The development of artificial neural network and deep learning algorithms in the last decade has provided a crucial development to solve complex mathematical modeling problems. The application of artificial neural network and deep learning algorithms to solve the complex flow problems arises in the reservoir simulation. The reservoir simulation is a complex phenomenon that requires understanding of the complex fluid flows phenomenonâ??s and solving nonlinear partial differential equations numerically. Artificial neural network and deep learning algorithm helps to simulate the flow phenomenon by reducing the numerical error and also useful in reducing the approximations in the mathematical model. This chapter deals with current state of the art literature on implementation of artificial neural network and deep learning algorithms in simulation reservoirs for enhanced oil recovery applications. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ID  - scholars15992
SP  - 59
TI  - Artificial Neural Network Modeling of Nanoparticles Assisted Enhanced Oil Recovery
ER  -