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ONLINE: Machine Learning and Data Science for Upstream Professionals - Module 1 (RES965)

  • 1-4 March 20214 daysVirtual instructor-led course, OnlineCourse Fee: 1350 EUR + VAT

Description

Module 1: Practical Scientific Programming for Upstream Professionals: Data Wrangling, Visualization, and Analysis

This course aims to provide upstream professionals with a comprehensive introduction to the main machine learning methods and builds hands-on experience in data science and machine learning.

This course is a prerequisite to attend the more advanced Module 2 as both courses are interconnected.

Course Structure: 4 modules of max. 4 hours each, delivered over 4 days
Each day will consist of a 4-hour module that includes 3 lessons (1 hour each) and multiple breaks.

Course Level: Skill
Instructor: Vitali Molchan

Designed for you, if you are...

  • A reservoir engineer, geologist or petrophysicist, and keen to obtain a fundamental understanding and practical knowledge on scientific programming, data science and machine learning

Participants should have upstream domain knowledge. Prior programming experience is a plus, but not required.

This course is a pre-requisite to attend Module 2 of this course to gain a strong knowledge of programming, statistics, and optimisation theory, which is essential to benefit from Module 2.

How we build your confidence

  • The main machine learning methods will be discussed and illustrated with multiple re-usable code examples and real data sets
  • Solutions of multiple problems related to reservoir engineering, geology and petrophysics will be demonstrated using state-of-the-art machine learning libraries

The benefits from attending

During this course you will learn how to:
  • Confidently use Python programming language and the main machine learning libraries to solve different problems from the upstream domain.
  • Create a powerful and reusable workflow for production data analysis from different sources (local files and production databases) that can be applied for small and large oil and gas fields.
  • Quickly prepare production and pressure data for material balance calculation for the reservoirs of the high level of complexity (multiple compartments and pressure datums) in the format of industry-standard software (PETEX MBAL).
  • Analyse a large number of reservoir simulation runs in an efficient way quickly getting insights into history matching quality and forecasting results.
  • Easily create high-quality visualization of different kinds of field and well data (production, pressure, well log) to simplify the data analysis and get ready-to-use plots for presentations and reports.

  • Apply different numerical optimisation methods to solve practical problems from the reservoir engineering domain (fitting rate-time data to understand the reservoir depletion mechanism, matching the reservoir pressure gradient with PVT data for consistent reservoir simulation model initialisation).
  • Perform smart upscaling of the fine grid static model into the coarse grid reservoir simulation model with precise control of the upscaling process and finding a trade-off between model dimensionality reduction and the level of geological details preservation.
  • Perform the probabilistic volume-in-place estimation taking into account the uncertainty of input parameters to quickly evaluate volumetrics without building a full scale geological model.
  • Allocate water and gas injection volume between injection wells to maximize oil production using the optimal number of reservoir simulation runs.

Topics

Day 1
  • Topics covered:
    - Introduction to Machine Learning ecosystem
    - Python refresher
    - Data wrangling (using Pandas and SQL) - Part 1
  • Exercises:
    - Basic Python
    - Production data analysis and visualization - Part 1

Day 2
  • Topics covered:
    - Data wrangling (using Pandas and SQL) - Part 2
    - Data visualisation (static and dynamic plotting)
  • Exercises:
    - Production data analysis and visualisation - Part 2
    - Data preparation for material balance calculations
    - Reservoir simulation model QC
    - Production data visualisation

Day 3
  • Topics covered:
    - Statistics refresher
    - Uncertainty evaluation and decision making
  • Exercises:
    - Volume-in-place probabilistic estimation
    - Static model upscaling

Day 4
  • Topics covered:
    - Numerical optimisation
  • Exercises:
    - Decline curve analysis
    - PVT data preparation for reservoir simulation
    - Waterflood optimisation

Customer Feedback

"I almost cancelled the course because I didn't believe that I will get the value from the course from online session. Luckily I didn't do it, the online session was amazing. The 4 hours per day is enough to drain all the brain power for the day and twice a week course enable us to train during the course. I would say this is even better than the original 5 days in the row. I can imagine with 8 hours/day for 5 days I will be highly saturated with information and would not be able to absorb as much as in comparison with the 4 hours 2x a week format. I am really thankful for this recommendation and option for the course timing." – Reservoir Engineer at Wintershall

"Excellent opportunity for subsurface professionals to dive into AI and machine learning. Lector has great teaching skills. The course has opened my eyes to how my daily engineering routine can be done more efficiently. Now I am well equipped with necessary knowledge as well as ready-to-use programming code." - Senior Reservoir Engineer at Belorusneft

"The course would not have been possible face to face because current project work would not have allowed my participation then. In the online mode, especially due to splitting the course over several weeks, allowed a good compromise to split course and project work. I also liked the ready-made workbooks. I can apply these to my own data without much changes." – Principal Geoscientist at HOT

"I liked the examples and the way the instructor explained the logic behind without going deeply into the codes." – Reservoir Engineer at Wintershall


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HOT Engineering GmbH   Tel: +43 3842 43 0 53-0   Fax +43 3842 43 0 53-1   hot@hoteng.com