Data Science and Machine Learning in Oil and Gas


Data science and machine learning are fast-paced emerging technologies across all industries including oil and gas. The oil and gas industry has taken big steps to adopt these technologies to make its exploration and production operations more efficient.

This course is tailored for oil and gas industry professionals who are interested to implement data analysis and machine learning in their project development and operations.

In attending this course, participants will learn:

  • Writing effective programs with Python language
  • Advanced data analysis and visualization of oil and gas data
  • Production data forecasting
  • Machine learning for lithofacies classification
  • Machine learning for well log synthesis

After taking this course, participants will gain comprehensive knowledge about data analysis and machine learning and be ready to implement these technologies in oil and gas operations and projects.


  1. Writing Effective Programs with Python Language
    • Using NumPy for numerical operation
    • Speeding up for-loop with list comprehension
    • Making scientific plots with Matplotlib
    • Data input and output with NumPy and Pandas
    • Interpolation with SciPy
  • Advanced Techniques of Data Analysis and Visualization
    • Loading spreadsheets and data cleansing
    • Techniques of data analytics with Pandas
    • Making bar graph, pie chart, box plot, and KDE plot from data
    • Interactive plotting with Plotly
    • Missing data issues and how to handle
  • Production Data Analysis and Forecasting
    • Time-series analysis of production data
    • Visualization of production data
    • Removing outliers of production data
    • Decline curve analysis step-by-step
    • Uncertainty analysis in decline curve analysis
  • Machine Learning Classification – Lithofacies Classification
    • Classification modeling workflows
    • Reading well log data and visualize lithofacies
    • Introduction to classifiers in Scikit-Learn
    • Classifier evaluation with cross-validation
  • Machine Learning Regression – Well Log Synthesis
    • Regression modeling workflows
    • Read well log data and visualize lithofacies
    • Introduction to regressors in Scikit-Learn
    • Regressor evaluation with cross-validation


This course is designed for all oil and gas professionals who want to learn the applications of data science and machine learning such as:

  • Petrophysicists
  • Geologists
  • Production engineers
  • Reservoir engineers
  • IT personnel
  • Petroleum engineers
  • Project engineers and managers
  • Computer programmers
  • Anyone who wants to learn Python language, data science and machine learning and their applications.


Yohanes Nuwara is an expert on data science and machine learning. He has published several articles on the applications of data science and machine learning on oil and gas production data analysis and forecasting. 

He had worked in the oil and gas industry as a geophysical engineer. Currently, he is Expert Data Scientist at Asia Pulp & Paper Sinarmas.

Yohanes Nuwara has provided consulting on data science and machine learning to oil and gas professionals from various companies worldwide such as PTTEP Vietnam, Shell, ExxonMobil, Schlumberger, and Pertamina.

He was a visiting lecturer for Python Data Science in the Petroleum Engineering Department of Marietta College in the US.

Yohanes Nuwara has taught courses for the Society of Petroleum Engineers (SPE). More than 1000 professionals and students worldwide have attended his courses.


  • The course will be delivered online (Zoom or Microsoft Teams)
  • Participants do not have to install Python on their computers/laptops
  • Participants will use Google Colab online for coding
  • Participants must access to strong internet connection


Course date: To be announced

COURSE FEE – To be announced


Please register here.

For more information about the course or inhouse training please email your message to LDI Training at