Machine Learning for Petroleum Engineers using Python

  LEVEL : BEGINNER                    HRDF : CLAIMABLE 

    TRAINERS : Dr Berihun Mamo Negash (UTP) & Mr Cheang Hoi Him (PETRONAS)

 

WHEN  


18 - 20 OCTOBER 2021

 

WHERE 


MS TEAMS

 

TIME


9.00AM - 5.00PM

 

RM 1,350 FOR PROFESSIONALS

10% Discount for Early Bird (until 18 September 2021) / Group / Students

CONTENT SUMMARY

INTRODUCTION

Digitization and machine learning are buzz words in the oil and gas industry. However, many petroleum engineers find it difficult to catchup with the implementation programming languages and machine learning concepts, even though they are very fluent in the physics-based understanding of the subject. This course aims to provide those engineers with a comprehensive introduction to Python programing and builds a hands-on experience on using Python as a tool for petroleum engineering applications. The course is designed to provide participant, the confidence to implement supervised machine learning algorithms in their day-to-day working life. It also paves the way to dig dipper and catch up the many other machine learning algorithms on their own. The course will begin by introducing Python and many useful libraries and proceeds to exploratory data analysis. Then it covers Regression and Classification methods as applied to problems encountered in petroleum engineering. Timeseries forecasting of production data, well log analysis and predictions followed by application of Artificial Neural Network (ANN) and Multiple regression will follow using a hands-on exercise. 

COURSE CONTENT

No

Topic

1
  • Introduction to python programming and libraries
  • Overview of Machine learning algorithms

             -   Exploratory data analysis with example

                      1.  Missing data

                      2. Outliers - 3-sigma rule

2
  • Supervised learning with hands-on exercise

             -     Regression

                      1.  Curve fitting [Scipy - optimize                                      (curve-fit)]

                      2. Linear regression [Scipy -                                            stats.linregress]

                      3. Polynomial regression [Numpy –                                polyfit]

                      4. Multiple regression [Sklearn linear-                            model (Linear regression)]

              -     Classification

                      1.  Logistic classification

                      2. Random forest

3
  • Other applications of machine learning in petroleum Engineering

              -     Time series forecasting

              -     Well Log analysis.

              -     Prediction of missing log

              -     Pore pressure prediction using ANN                          (compare with polynomial and Multiple                      regression)


OBJECTIVES

Upon completion of this course, participants will be able to:

  • Discover patterns, spot anomalies and test hypothesis with the help of summary Exploratory data analysis which employs statistics and graphical representations.
  • Apply Regression and Classification algorithms using free open source python libraries.
  • Perform time series forecasting of production and pressure data.
  • Explore the application of machine learning algorithms for selected problems which maybe encountered in the day to day life of a petroleum engineer. 


WHO SHOULD ATTEND?

  • Petroleum Engineers 
  • Geologists 
  • Petrophysics
  • Anyone who want to uncover the application of machine learning in petroleum engineering.






OUR TRAINERS




1. Dr Berihun Mamo Negash  (UTP)

Dr Berihun Mamo Negash is a senior lecturer at Universiti Teknologi PETRONAS. He has been researching and working on machine learning and its application in the petroleum engineering field for over five years. In addition to experiment-based works, he has also published few articles in areas of applied machine learning. Proxy modeling of a reservoir using system identification concept and missing production and pressure data reconstruction are his main area of specializations


2. Mr Cheang Hoi Him  (PETRONAS)

Mr Cheang Hoi Him is a data scientist in PETRONAS Digital Sdn Bhd. He graduated from Universiti Teknologi PETRONAS as Petroleum Engineering graduate and has 3 years working experience in PETRONAS solving industry pain points with digital approach and data science methodology. His area of expertise is Python programming, building machine learning model in Python, cloud computing and model deployment.



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REGISTRATION FEES

      ProfessionalS       

myr1,350

*fee quoted does not include SST, GST/VAT or withholding tax (if applicable)

Early bird/ group/ student

myr1,215

*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