Machine Learning and Deep Learning Modeling of Reservoir Characterization and Reservoir Simulations Using Wireline Logs
LEVEL : Beginner HRDF : CLAIMABLE
TRAINER : ASSOC. PROF. DR. ESWARAN &
DR. SAYED AMEENUDDIN
WHEN
25 MARCH 2021
WHERE
MS TEAMS
TIME
9.00AM - 5.00PM
RM 320 FOR PROFESSIONALS
10% Discount for Early Bird (until 24 Feb) / Group / Students
CONTENT SUMMARY
INTRODUCTION
Understanding the machine learning and deep learning models are an essential part of simulations, and the advancement in these fields has given greater advantage for new simulation techniques. The proposed training utilizes the machine learning and deep learning-based modeling method for developing a novel algorithm for prediction of reservoir parameters such as TOC permeability, porosity and using wireline logs. The wireline logs used for the prediction algorithm are consist of bulk density, resistivity, depth, gamma-ray, and compressional time. This case study deals with the deep learning model. The model can be developed using the wireline logs from different parts of the world having different geophysical properties.
- Introduction to machine learning and deep learning.
- Understanding of the machine learning and deep learning models for reservoir simulations.
WHO SHOULD ATTEND?
- Managers, Executives, Engineers, Geologists, Researchers & Consultants who are interested in Machine learning and Deep learning as applied to Petroleum Systems.
OBJECTIVES
Upon completion of this course, participants will be able to:
- Understating the concepts of Machine learning and deep learning.
- Case study to predict the reservoir characterization using wireline logs.
1. Associate Professor Dr Eswaran
Padmanabhan (UTP)
Dr Padmanabhan is a head of Shale Gas Research Group and senior faculty member in Department of Petroleum Geoscience, Universiti Teknologi PETRONAS. Dr Eswaran completed his BSc in Geology (Hons) in 1988 at the University of Malaya, and later obtained a Master in Science from Rijksuniversiteit Ghent, Belgium in 1991. He obtained his PhD from the University of Saskatchewan in 1995. He joined the Department of Geosciences UTP in January 2010. He teaches Geochemistry, Mineralogy and Petrography and Regional & Petroleum Geology of Malaysia and SEA. His research interest and consultancy work includes surface chemistry, dissolution kinetics and sorption-desorption phenomena. He is currently the Director of the MOR - EOR research consortium. His area of expertise in Mineralo-Chemistry.
Dr Firas is a Post-Doctoral Researcher in Shale Gas Research Group working on developing machine learning and deep learning models for TOC, permeability and adsorption and desorption modeling and kinetic data analysis actively involved in the developing mathematical model for fracture propagation in the shale. He has completed his PhD in Applied Mathematics at UTP. His main area of research is mathematical modeling and simulation for Oil and Gas applications. Dr. Irfan has published several reputed journal articles on mathematical modeling and simulation Techniques.
REGISTRATION FEES
ProfessionalS
myr320
*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)
Early bird/ group/ student
myr288
*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
DROP US AN EMAIL
cape@utp.edu.my