SPE Workshop: Subsurface Data Analytics 27 - 28 Oct 2020 Pomeroy Kananaskis Mountain Lodge Kananaskis, Alberta, Canada

Agenda

Monday, October 26

08:00 - 17:00
Training Course: Fundamentals of Machine Learning and Data Science for the Oil and Gas Industry
Ticketed Event
Instructor(s) Marcelo Guarido

Machine learning is a growing field in technology companies, and more recently, in the Oil and Gas industry companies and research groups. This course focuses on the data preparation and machine learning application on different case of study examples:

  • Introduction to Data Science in Python and R
  • Predicting mass of oil (regression)
    • Data analysis
    • Data cleaning
    • Linear regression
    • Gradient boosting regressor
    • Interpretation
  • Facies classification
    • Data analysis
    • Data imputation
    • Feature engineering
    • Logistic regression (imbalanced data)
    • Gradient boosting classifier (imbalanced data)
  • Severe injuries analysis and predictions (HSE)
    • Natural language processing
    • TF-IDF
    • Reports classification
  • Deep Learning
    • Introduction to Tensorflow
    • Image segmentation for salt identification in seismic sessions

Learning Objectives

  • Python programming: tips for machine learning modeling
  • R programming: data analysis and visualization
  • Data preparation: how the data must be processed prior to modeling to improve product outcome
  • Machine learning: learning the most popular ML packages for python, such as Pandas, Scikit-Learn, Tensorflow, among others
  • Interpretation of the model used, and how to choose the most appropriate model for each case

Learn more about this course.

08:00 - 17:00
Training Course: From Data Gathering, Critical Statistics to Predictive Modelling: Execution Steps of a Data Science Project Applied to Oil and Gas
Ticketed Event
Instructor(s) Peter Dimmell

This one-day course will teach the fundamentals of data science project design and execution, including data collection principles and statistics through Design of Experiments (DOE). By engaging participants in hands on, dynamic experiments covering data collection, model selection, model assessment, simulation and forecasting; students will gain a foundational understanding of the key principles of the analytics project life cycle. The physical and collaborative nature of this experience will ingrain a tangible, unforgettable understanding of the critical concepts that successful analytics projects require.

Learning Objective: Exposure to advanced statistics, Design of Experiments and Machine Learning concepts will be covered in this course. This course will provide an engaging experience for the class participants with hands on, dynamic experiments covering data collection, model selection, model assessment, simulation and forecasting; students will gain a foundational understanding of the key principles of the analytics project life cycle.

Learn more about this course.​

18:00 - 19:30

Tuesday, October 27

07:00 - 08:00
07:00 - 08:00
08:00 - 08:15
Opening Remarks
Moderator(s) Graham Cain, Cenovus Energy; Ali Esmail, Encana
08:15 - 10:00
10:00 - 10:30
10:30 - 12:00
12:00 - 15:00
15:00 - 16:30
16:30 - 17:00
17:00 - 18:30
18:30 - 21:00