DAT 608
Machine Learning Guide for Oil and Gas Using Python
Machine Learning Guide for Oil and Gas Using Python
COURSE SCHEDULE
| Code | Date | Location | price (€)* |
|---|---|---|---|
| DAT 608 | 23 - 27 Oct 2023 | Online | 1790 |
| DAT 608 | 21 - 25 Oct 2024 | Online | 1990 |
| DAT 608 | 15 - 23 Aug 2024 | Stavanger | 3990 |
* Prices are subject to VAT and local terms. Ph.D. students, groups (≥ 3 persons) and early bird registrants (8 weeks in advance) are entitled to a DISCOUNT!
COURSE OVERVIEW
In this course, we will teach participants various machine learning techniques and tools necessary to train, test, and apply a model from scratch. We will use open source and easy to learn language such as Python to build workflows using different libraries (pre-built code samples) to perform various tasks in an interactive session. In other words, we make the entire coding experience and set up easy, pleasant, and straightforward. There is no need to have any experience in machine learning, data science, linear algebra, or coding. We will teach every step of the way, anyone in the organization who has the slightest passion for implementing AI, ML and deciphering essential information from the data is welcomed to attend. We will also accommodate those who have a higher level of experience with implementing AI and ML using python.
COURSE OUTLINE
5 days
Day 1
o Machine Learning and Python applications
o Python installation (Anaconda installation)
o Jupyter Notebook interface and functionalities
o NumPy
Day 2
o Pandas data frame processing with completions data set examples
o Data visualization with Matplotlib and Seaborn
Day 3
o Data preprocessing
o Model Building for real-time drilling and production applications
o Unsupervised machine learning
Day 4
o Introduction to predictive model characteristics
o Linear Regressions for production optimization
o Logistic Regression for geologic facies classification
o K-Nearest Neighbor (KNN) for geologic log imputation
o Decision Trees (DT) and Random Forest (RF) for completions design optimization
o Support Vector Machine (SVM)
Day 5
o Neural Networks for sonic log generation and CUM/ ft production forecasting
o Model Evaluation for drilling, completions, reservoir, etc.
INSTRUCTOR
Dr. Hoss Belyadi
Hoss Belyadi is the founder and CEO of Obsertelligence, LLC, focused on providing artificial intelligence (AI) in-house training and solutions. Mr. Belyadi has served as an adjunct faculty member at multiple universities, including West Virginia University, Marietta College, and Saint Francis University. There, he taught data analytics, natural gas engineering, enhanced oil recovery, and hydraulic fracture stimulation design. Mr. Belyadi has over 12 years of experience working in various conventional and unconventional reservoirs across the world. He has worked on various machine learning projects and held short courses across various universities, organizations, and the department of energy (DOE). Mr. Belyadi is the primary author of “Hydraulic Fracturing in Unconventional Reservoirs (1st and 2nd editions) and is the author of “Machine Learning Guide for Oil and Gas Using Python.” Hoss earned his BS and MS, both in petroleum and natural gas engineering from West Virginia University.
FAQ
DESIGNED FOR
o Engineers, software developers, data scientists, data engineers, data enthusiasts, business analysts, financial analysts, technical support, university professors, and even executives that would like to learn about this fascinating field
o Anyone in the organization who has the slightest passion for implementing AI, ML
o Advanced Python and ML users
COURSE LEVEL
o Beginer to Intermediate
LEARNING OBJECTIVES
You will gain new knowledge and experience in these things:
* Writing effective programs with Python language
* Learning the basics and some advanced techniques of data analysis and
visualizations
* Well-log data visualization and petrophysical analysis
* Numerical methods for reservoir engineering
* Production data analysis and forecasting
REGISTER
Registration is now OPEN!
* Prices are subject to VAT and local terms. Ph.D. students, groups (≥ 3 persons) and early bird registrants (8 weeks in advance) are entitled to a DISCOUNT!
For more details and registration please send email to: register@petro-teach.com
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