21 April - 11 May 2020
About this course
Interest in data science and machine learning is rapidly expanding, offering the promise of increased efficiency in E&P, and holding the potential to analyse and extract value from vast amounts of under-utilised legacy data. Combined with petroleum geoscience and engineering domain knowledge, the key elements underlying the successful application of the technology are: data, code, and algorithms. This course builds on public datasets, code examples written in Python, and algorithms from popular data science packages to provide a practical introduction to the subject its application in the E&P domain.
Who should attend?
This is an introductory course for reservoir geologists, reservoir geophysicists, reservoir engineers and technical staff who want to learn the key concepts of data science and its application to E&P data. By developing your data science skills you’ll be better equipped to analyse your project data, build predictive models and apply them in your workflows. You’ll also be able to evaluate and ask the right questions about the models created by others, be they in-house data science specialists or external partners.
What you’ll learn
The course comprises a mix of lectures and hands-on computer workshops. You’ll gain a basic working knowledge of coding in Python and the use of a toolset for importing, visualizing and building models from data. You’ll also gain a powerful working environment for data science on your laptop, which together with code examples provided by the course will give you a jump start to applying the techniques you’ll learn to your own projects.
David Psaila has over 20 years experience in the oilfield services industry, developing technology for reservoir geophysics, geostatistics, reservoir modelling, data analysis, and machine learning. He has experience developing and teaching training courses on technical subjects including reservoir geostatistics and stochastic seismic inversion. David has extensive worldwide experience of reservoir studies including quantitative time-lapse interpretation, pore pressure prediction, and stochastic seismic inversion. He holds a degree in Geology from the University of Oxford and a PhD in Geophysics and Geostatistics from the University of Leeds.
Dates & Times
Part 1: 21st April. Introduction to Data Science
An overview of the data science process and how it can be applied to E&P data.
Hands-on experience of the data science toolset and coding in Python.
Part 2: 28th April. Exploratory Data Analysis.
An introduction to exploratory data analysis, visualization tools, and descriptive statistics.
Hands-on experience of visualization and performing exploratory data analysis, including descriptive statistics, data cleaning, and data transformations.
Part 3: 5th May. Supervised Machine Learning
An introduction of supervised machine learning, including algorithms for classification and regression, their advantages and limitations,
Hands-on experience of machine learning, building and evaluating supervised models.
Part 4: 11th May. Unsupervised Machine Learning
An introduction of unsupervised machine learning, including algorithms for outlier detection and clustering, their advantages and limitations.,
Hands-on experience of machine learning, building and evaluating unsupervised models.
What are the prerequisites?
The course is at an introductory level and all subject matter will be taught from scratch. No prior experience of statistics, coding or machine learning is required, although knowledge of basic maths and statistics is useful. Hands-on computer workshops form a significant part of this course, and participants must come equipped with a laptop computer running Windows (7, 8, 10) or MacOS (10.10 or above) with sufficient free storage (4 Gb).
PESGB - Training Room