Machine and Deep Learning Applications in Oil & Gas
LEVEL : INTERMEDIATE HRDF : CLAIMABLE
TRAINERS : ASSOC. PROF. DR. IBRAHIMA FAYE &
DR ERIC HO TATT WEI
WHEN
1 - 4 NOVEMBER 2021
WHERE
MS TEAMS
TIME
9.00AM - 5.00PM
RM 3,380 FOR PROFESSIONALS
10% Discount for Early Bird (until 1 October 2021) / Group / Students
CONTENT SUMMARY
INTRODUCTION
Traditional machine learning techniques have been used in various O&G applications. Over the last few years, many researchers have shifted from traditional machine learning to deep learning techniques due to their promising applications. The course will start with an overview of traditional machine learning techniques and show how they have been implemented in O&G It will then introduce the basic principles of deep neural and convolutional neural networks (CNN) along with some of the well-known architectures that have been developed. Examples of machine deep learning applications will be given in Petrophysics, Seismic fault detection, seismic image generation, super-resolution seismic imaging, oil production forecasting, etc.
- Overview of traditional machine learning techniques
- Basic principles of deep neural and convolutional neural networks (CNN)
- Overview of some developed architectures.
- Various O&G related Examples
- Hands-on with examples (signals and images)
OBJECTIVES
Upon completion of this course, participants will be able to:
- Know basic traditional machine learning techniques used in O&G
- Understand the basic principles of deep learning
- Apply Machine and Deep learning techniques in various O&G
WHO SHOULD ATTEND?
- Anyone interested in Machine learning
- Engineers, Researchers and graduate students
1. Associate Prof Dr. Ibrahima Faye (UTP)
Associate Professor at the Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS (UTP) Research Interest: Engineering Mathematics, Signal & Image Processing, Pattern Recognition, and Dynamical Systems.
Ibrahima Faye is a researcher at the Centre for Intelligent Signal and Imaging Research (CISIR), a national centre of excellence. He received a BSc, MSc and PhD in Mathematics from University of Toulouse and a MS in Engineering of Medical and Biotechnological Data from Ecole Centrale Paris. His research interests include Machine learning, Mathematics, Signal and Image Processing, Science of Learning. He has published over 150 papers in peer reviewed journals and international conferences and holds 2 patents in Image Processing. He is a senior member of the IEEE and currently the vice-chair of IEEE Computational Intelligence Society (CIS), Malaysia.
2. Dr Eric Ho Tatt Wei (UTP)
Senior Lecturer at Electrical and Engineering Department, Universiti Teknologi PETRONAS Experts on deep neural network technology to network analysis on MRI brain images Eric Ho Tatt Wei received his MS and PhD degrees in Electrical Engineering from Stanford University in Silicon Valley, USA specializing in computer hardware and VLSI systems, As part of his PhD research, he developed real-time systems for fruit flies for biological research to conduct automated inspection and guide robotic manipulation. He is currently pursuing applications of deep neural network technology to network analysis on MRI brain images.
REGISTRATION FEES
ProfessionalS
myr3,380
*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)
Early bird/ group/ student
myr3,042
*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)
OUR LOCATION
Centre for Advanced & Professional Education (CAPE)
Level 16, Menara 2,
Menara Kembar Bank Rakyat,
50470, Jalan Travers,
Kuala Lumpur.
CALL US
+605 - 368 7558 /
+605 - 368 8485
DROP US AN EMAIL
cape@utp.edu.my