Essential Data Science for Petroleum Geoscientists and Engineers

Module 1. Overview

• What is Data Science - Overview of the course, and an outline of the scope of data science.

• Data Science for E&P - Addressing the role of data science in E&P and an example application to log

data quality control and reconstruction using machine learning.

Module 2. Data Science Toolkit - Notebooks, Visualization, and Communication

• Overview of the data science toolkit.

• Hands-on workshop introducing the toolkit and getting started with Python scripts and notebooks.

• Overview of how to manage and use Python packages.

• Hands-on workshop on Python packages covering how to install and manage packages, and how to use

packages from your Python notebooks.

• Introduction to data visualization with SandDance.

• Hands-on workshop introducing SandDance for interactive data visualization using a dataset of offshore

wells from the UK Continental Shelf.

• Overview of Markdown, a lightweight markup language for adding simple formatting to plain text

documents, and documenting Python notebooks.

• Hands-on workshop on Markdown for formatting text documents and annotating Python notebooks.

Module 3. Python Fundamentals

• Python 101 - Introduction to Python fundamentals including variables, types, statements, expressions,

control flow, and functions.

• Hands-on workshop on Python 101.

• Python 102 - More Python fundamentals including modules, files and folders, data structures, and data

frames.

• Hands-on workshop on Python 102.

Module 4. Computational Thinking

• Introduction to Computational Thinking - the analytical and logical processes of decomposing a complex

task and expressing it in a form that can be performed by a computer.

• Hands-on workshop on Computational Thinking applied to the design and implementation an interactive

base map for UK E&P data.

Module 5. Exploratory Data Analysis

• Exploratory Data Analysis - Introduction to the Exploratory Data Analysis process and key Python

packages for data analysis and statistical graphics.

• Hands-on workshop on exploratory data analysis - reading data, handling dates, merging datasets,

creating statistical graphics figures, exporting results.

• Statistical Graphics - Demonstration of a gallery of statistical graphics samples.

• Descriptive Statistics - Introduction to univariate and multivariate statistics.

Module 6. Exploring E&P Data

• Well header data - Introduction to handling well header data (surface location and attributes) using the

pandas and plotly packages.

• Hands-on workshop on well header data - including import, data cleaning, date handling, posting well

data on cultural/satellite base map and visualizing historical trends.

• Production data - Introduction to handling field production data using the pandas and plotly packages.

• Hands-on workshop on field production data - including import, data cleaning, date handling, queries,

visualizing hierarchical and time series data.

• Well log data - Introduction to handling wireline logs from LAS files using the lasio, pandas, and plotly

packages.

• Hands-on workshop on well log and tops data - including LAS file import, merging tops, and data

visualization.

• Seismic data - Introduction to handling seismic SEG-Y data using the segyio, and plotly packages.

• Hands-on workshop on seismic data - including SEG-Y file import, extracting binary and trace headers,

visualizing seismic trace data, and calculating seismic attributes.

Module 7. Geospatial Data

• Coordinate reference systems - Introduction to geographic and projected coordinate systems, defining a

coordinate reference system from EPSG codes, offsets between coordinate reference systems, and

transforming positions between reference systems.

• Hands-on workshop on coordinate reference systems.

Module 8. Machine Learning Fundamentals

• Machine Learning - introduction to 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.

• Supervised learning with regression - introduction to regression including random forest regression and

performance evaluation.

• Hands-on workshop on regression for reconstructing wireline logs.

• Unsupervised Learning - introduction to unsupervised learning for dimensionality reduction, clustering

and outlier detection.

• Hands-on workshop on dimensionality reduction for wireline logs.

• Explainable Machine Learning - introduction to explainable machine learning: techniques for looking

inside the so-called black box models of machine learning to understand why particular predictions are

made and which variables are important.