Short Course Catalogue - Data Science

 


Machine Learning

Introduction to Machine Learning (ML) for Geophysics

Instructor: Jaap Mondt (Breakaway)

More and more Machine Learning (ML) will play a role in the geosciences. ML resorts under the overall heading of Artificial Intelligence. In this context also the terms “Algorithms” or “Big Data” are often mentioned. Many scientists mention “Let the data speak for itself” when referring to ML, indicating that hidden or latent relationships between observations and classes of (desired) outcomes can be derived using these algorithms. A clear example is in the field of Quantitative Interpretation.

The aim of this 1-day course is to convey how ML is used in geophysical applications. It will give an understanding of the “workflows” used in ML. The used algorithms can be studied separately using references. Power-point presentations will introduce various aspects of ML, but the emphasis is on computer-based exercises using open-source software. The course concerns a genuine geophysical issue: predicting lithology and pore fluids, including fluid saturations. The input data are Acoustic and Shear Impedances, Vp/Vs ratios and AVA Intercept and Gradients. The exercises deal with preconditioning the datasets (balancing the input classes, standardization & normalization of data) and applying several methods to classify/predict data: Bayes, Logistic, Multilayer Perceptron, Support Vector, Nearest Neighbour, AdaBoost, Trees. This for supervised/labelled as well as unsupervised applications. Non-linear Regression is used to predict fluid saturations.

More information

Machine Learning for Geoscientists with Hands-on Coding

Instructor: Dr. Ehsan Naeini (Ikon Science)

The objectives of this short course are: 1) show various Geoscience examples in which machine learning algorithms have been implemented, 2) teach the basic principles of machine learning and deep learning, 3) demonstrate the flexibility of coding machine learning in Python. Trainees will code a classification and a regression algorithm during the class using freely available Python libraries. The course is for entry level practitioners and involves hands-on coding, hence having some Python skills is an advantage but not essential.

More information

Machine Learning in Geosciences

Instructor: Mr. Gerard Schuster (King Abdullah University of Science and Technology)

Participants will learn the high-level principles of several important topics in machine learning: neural networks, convolutional neural networks, and support vector machine. Practice the execution of these methods on MATLAB codes and Python-related codes. Applications include fracture detection in photos, fault delineation in seismic images and picking NMO velocities in semblance gathers.

More information

Machine Learning in Geosciences - 2 days

Instructor: Mr. Gerard Schuster (King Abdullah University of Science and Technology)

Participants will learn the high-level principles of six important topics in machine learning: neural networks, convolutional neural networks, support vector machines, principal component analysis, and clustering methods. They will practice the execution of these methods on MATLAB codes (free for 30 days after downloading it from the MATLAB site) and Python-related codes (can be uploaded during the course). Applications include fracture detection in photos, fault delineation in seismic images, coherent noise elimination in migration images, and picking NMO velocities in semblance gathers.

More information

New Applications of Machine Learning to Oil & Gas Exploration and Production

Instructor: Dr. Bernard Montaron (Fraimwork SAS)

The course introduction will attempt to answer the question: How will A.I. change the way we work in the Oil and Gas industry in the coming years? Looking at what is underway in other industries and guessing what type of projects are under development in R&D departments in our industry will help answer that question.

Oil and Gas examples will be presented corresponding to each of the terms A.I., Machine Learning, and Deep Learning, allowing participants to reach a clear understanding on how they differ.
The course will then focus on Deep Learning (DL) and address all key aspects of developing and applying the technology to Oil and Gas projects.

More information

--