ADGM Academy

Discover who we are, what drives our mission, and how we empower talent through industry partnerships, innovative learning, and programmes that strengthen the region’s financial and business landscape.

Read more
Featured Highlights ADGM Academy Host High Level Roundtable on the Future Financial Workforce Under the patronage of Theyab bin Mohamed bin Zayed, 15 UAE Nationals to join National Talent Development with World Bank Group Explorers Program
ADGM Academy Schools

Explore ADGM Academy’s schools, where industry aligned programmes, expert led learning, and practical skill development come together to prepare professionals for success in today’s fast evolving business landscape.

Explore all ADGM Academy Schools
Featured Schools Developing expertise for the evolving world of sustainable finance Preparing professionals to lead in evolving treasury and risk functions
Explore our Programmes

Explore a wide range of industry aligned programmes designed to build future ready skills, support professional growth, and empower individuals and organizations to thrive in today’s rapidly evolving financial and business landscape.

Explore our Programmes
Featured Highlights Delivering structured assessments that strengthen capability and guide professional growth Modern facilities equipped to host impactful meetings and events
Explore our Tech Centre

Abu Dhabi’s multi-sector innovation hub – a first-of-its-kind ecosystem that fuses education, research, regulation, and investment under one roof. We bring together policymakers, regulators, industry leaders, researchers, and entrepreneurs to shape the technologies and industries of tomorrow.

Explore more
Latest Publications Quantum Computing in Digital Capital Markets Blueprints for the Future: RealAssetX Abu Dhabi Innovation Showcase
Explore our Knowledge Library

Our research focuses on five strategic themes that are redefining the future of financial services and the broader economy. Through authoritative papers, insights, and reports, we turn complex challenges into practical knowledge that supports industry leaders, regulators, and entrepreneurs.

Explore more
Upcoming events The Rise of Family Offices in the Middle East Financing Water Infrastructure: From Blue Bonds to Broader Capital Mobilisation Financing the Water Transition: Country Water Compacts & National Investment Platforms
EnerTech Lab

Practical Advanced Analytics and AI for Oil & Gas Professionals

Tech Centre EnerTech Lab Practical Advanced Analytics and AI for Oil & Gas Professionals

Programme Overview

This highly practical programme provides upstream professionals with a comprehensive introduction to the main machine learning methods and builds hands-on experience in data science and machine learning.

Through the course, you will develop a solid understanding of supervised and unsupervised learning algorithms including advanced topics such as deep learning and machine learning models explainability.

The course is designed to build up your confidence from scratch: starting with an introduction of each method in simple terms, followed by detailed guidelines on how to apply different machine learning methods for solving actual problems from reservoir engineering, geo-modelling, and petrophysics. The knowledge obtained from the course - in combination with carefully designed code examples - can be applied by the participants in ongoing and future projects, thus increasing their overall performance.

Programme Benefits Programme Modules ADGM Academy HOT Certificate of Completion Five Days
Programme Benefits
You will feel confident in your understanding of core concepts of machine learning data science
Identifying existing bottlenecks for machine learning methods application in your professional domain machine learning methods
Choosing the most appropriate machine learning methods to solve a particular problem
You will be able to apply the main machine learning methods in practice main machine learning methods practice
Who is the Programme for?

This programme is designed for reservoir engineers, geologist or petrophysicists, as well as professionals who are keen to obtain a fundamental understanding and practical knowledge on scientific programming, data science and machine learning. Participants should have strong upstream domain knowledge with a minimum of 5 years’ experience. Prior programming experience (Python) is of advantage. Recommended pre-reading material on Python can be provided upon request.

Registration Form

Register Now
* Please note: This programme is available only to corporate groups. Individual signups are not eligible.
Learning Modules

Topics

  • Introduction to the machine learning ecosystem
  • Brief introduction to Python (crash course)
  • Data wrangling (using Pandas and SQL)
  • Data visualisation

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 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 visualisation 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

Topics

  • Numerical optimisation
  • Statistics refresher
  • Exploratory data analysis
  • Uncertainty evaluation and decision making

You will learn how to

  • Apply different numerical optimisation methods to solve practical problems from 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 maximise oil production using the optimal number of reservoir simulation runs

Topics

  • Machine learning introduction
  • Dimensionality reduction methods
  • Clustering methods
  • Anomaly detection methods

You will learn how to

  • Confidently apply machine learning terminology and identify technical and business requirements for successful application of machine learning methods
  • Choose the most suitable machine learning method to solve a particular problem from the upstream domain depending on the type of the problem, data availability, data quality and solution requirements
  • Perform screening of static model scenarios to simplify the history matching process, reduce the number of simulation runs and efficiently evaluate the impact of geological uncertainty on production forecast
  • Identify the optimal number of electrofacies for a modelling study to guide the distribution of properties in the reservoir model
  • Develop fully automated workflows to detect abnormal behaviours in production wells, helping prevent production losses and reduce environmental risks

Topics

  • Machine learning core concepts
  • Regression methods
  • Tuning of machine learning models

You will learn how to

  • Design and perform machine learning study to ensure the solution quality and reproducibility of the modelling results
  • Apply on practice and understand the main concepts of machine learning modelling: train/test split, cross-validation, objective function definition, bias-variance trade-off, hyperparameters tuning
  • Predict the performance of a new well and optimise the well completion design for unconventional reservoirs without building a sound physics-based reservoir simulation model
  • Develop a powerful data-driven model incorporating available fluid studies and predict the saturation pressure with high accuracy for the reservoirs with missing key PVT experiments
  • Automatically find the combination of machine learning model parameters to simplify the model tuning and reduce the amount of manual efforts

Topics

  • Classification methods
  • Neural networks and Deep learning
  • Advanced machine learning topics:
    • Imbalanced datasets
    • Interpretability of machine learning models

You will learn how to

  • Explain machine learning modelling results to technical and business audience to perform QA/QC solution and support decision making
  • Develop a robust classification model for lithofacies identification based on well logs for wells without core data
  • Create enhanced oil recovery screening model that allows incorporating different sources of information (PVT, SCAL, geological data), performing screening of a company’s fields portfolio in an efficient way and identifying the most suitable EOR method for a particular field

Programmes You May Be Interested In

About

Who we are Why ADGM Academy The Alumni Network Leadership Faculty Academy Centres News

Schools

Overview Banking & Finance Digital Assets Digital Literacy Entrepreneurship Law Management National Development Programmes Sustainable Finance Taxation Treasury Wealth & Asset Management

Offering

Qualification Examination Certification & Coaching Digital Learning Platform Venue Booking

Research & Tech

Research Centre Tech Centre Publications Cyber Arena Freedom Investment Lab FutureTech 4.0 EnerTech RealAssetX Abu Dhabi Research Themes Advisory Board

Resources

Events Blogs & News Contact Us Location
© 2026 ADGM. All rights reserved. Terms and Conditions

We use cookies and similar technologies that are necessary to operate the website. Additional cookies are used to perform analysis of website usage. By continuing to use our website, you consent to our use of cookies. For more information, please read our Cookies Policy.

Reject Accept