Unsupervised Deep Learning Algorithm to Solve Sub-Surface Dynamics for Petroleum Engineering Applications
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 d...
Saved in:
| Main Authors: | Kumar, A., Ridha, S., Ilyas, S.U. |
|---|---|
| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.29861 / |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2020
|
| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097532657&doi=10.1109%2fICCI51257.2020.9247667&partnerID=40&md5=e36cbbf912af01597fca1517fa1c4306 http://eprints.utp.edu.my/29861/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Machine learning methods for herschel-bulkley fluids in annulus: Pressure drop predictions and algorithm performance evaluation
by: Kumar, A., et al.
Published: (2020) -
Application of deep learning technique to predict downhole pressure differential in eccentric annulus of ultra-deep well
by: Krishna, S., et al.
Published: (2021) -
Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources
by: Kumar, A., et al.
Published: (2021) -
Application of boundary-fitted convolutional neural network to simulate non-Newtonian fluid flow behavior in eccentric annulus
by: Kumar, A., et al.
Published: (2022) -
Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: A comprehensive review
by: Krishna, S., et al.
Published: (2020)