Tutor(s)
David Psaila: Director of Data Science for the Digital Subsurface, Analytic Signal Limited.
Overview
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, statistical graphics, and algorithms from popular data science packages to provide a practical introduction to the subject and its application in the E&P domain.
Duration and Logistics
Classroom version: 5 days consisting of lectures and computer-based exercises and practicals.
Virtual version: Ten, 3-hour online sessions presented over 5 days. The course is at an introductory level and all subject matter will be taught from scratch. No prior experience of statistics, Python coding or machine learning is required, although some basic college level knowledge of 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 (8, 10, 11) or MacOS (10.10 or above) with sufficient free storage (4 Gb). Detailed installation instructions are provided in advance so that participants can set up their computer with the data science toolkit and course materials before the course starts.
Level and Audience
Fundamental. This is an introductory course for reservoir geologists, reservoir geophysicists, reservoir engineers, data management, and technical staff who want to learn the key concepts of data science.
Objectives
You will learn to:
- Analyse project data using the data science toolkit; notebooks, visualization, and communication.
- Perform data import and manipulation, data visualization, exploratory data analysis, and building predictive models from data.
- Have a working knowledge of coding in Python.
- Coordinate reference systems including geographic and projected coordinate systems.
- Use the fundamentals of machine learning including background concepts, the different types of machine learning, and the basic workflow to build and evaluate models from data.
