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PESA QLD online course: Data Science E&P Bootcamp
August 9, 2021 @ 1:00 pm - 5:00 pm AEST
There is an upcoming Online Data Analytics bootcamp that PESA QLD are offering (run by Halliburton).
The course will be five 3 hr online sessions across the week starting 9th August. I have included the session overview below (more details in link).
|Presentation:||Data Science E&P Bootcamp Online Course 2021|
|Venue:||Participants will be provided with a MS Team link. Trainers will be broadcasting from
|Date & Time:||Session 1 – Mon Aug 9, 1:00-5:00pm (AEST)
Session 2 – Tue Aug 10, 1:00-4:00pm (AEST)
Session 3 – Wed Aug 11, 1:00-4:00pm (AEST)
Session 4 – Thu Aug 12, 1:00-4:00pm (AEST)
Session 5 – Fri Aug 13, 1:00-4:00pm (AEST)
|Cost:||PESA Members: $950.00Non- Members: $1150.00
Student/Retired Members (max 2): $750.00
Data Science E&P Bootcamp Online Course 2021
Session #1: General Theories of Data Science Including O&G Case Studies and Basic Theories of Artificial Neural Networks (4 hours)
This session will introduce the participants to data sciences, machine learning and artificial intelligence in the light of energy industry. The focus will be on the concepts of different machine learning techniques and algorithms in general. Additionally, a few real case studies applicable to solve energy industry problems will be discussed to make participants understand the concepts. Also, the session will discuss artificial neural networks (ANN) concepts to the participants, as ANN finds numerous applications for AI/ML driven solutions implementation. This session caters the conceptual understanding of different architectures of neural networks, namely, CNN, RNN, MLP, etc.
Session #2: Exercise – Facies Classification trough Data Driven Well Log Analysis (3 hours)
Supervised / Unsupervised approach will be used in this hands-on exercise for facies interpretation using well log data. Facies classification finds a valuable application to determine the reservoir or non-reservoir facies. Data driven approach will emphasise the value addition besides conventional petro-physical approach of solving a similar problem.
Session #3: Exercise – Well Log prediction from Seismic Attributes (3 hours)
A supervised machine learning technology will be used for synthetic log generation in this exercise. A novel approach will be discussed in this exercise, so the participants can generate their own synthetic log. These logs can be used further for static reservoir modelling. This exercise will also help the participants to understand the hyper-parameters of model tuning aspects.
Session #4: Exercise – Fossil Classification through Computer Vision Image Analysis (3 hours)
Computer vision will be used to do some image analysis of fossils. This supervised method can be very useful for the participants for implement the similar model for well core sections for identifying biomarkers or do bio-stratigraphic interpretation.
Session #5: Exercise – Short Term Production Prediction through Machine Learning Model (3 hours)
Prediction of the oil or gas production is the key for successful hydro-carbon business. Machine learning based approach finds a very good application in this scenario. In this exercise, the participants will earn about the concept of time series analysis and ANN will be used to predict the gas rate for time series data.