Specialized Training Center | Introduction to Subsurface Machine Learning

Overview
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Introduction

Looking to understand machine learning and how it can be applied to subsurface analytics workflows?

This course is a foundational introduction to the landscape of subsurface-focused machine learning. Topics and techniques covered include outlier detection, data debiasing and imputation, feature engineering, anomaly detection, supervised and unsupervised learning, spatiotemporal modeling, and uncertainty modeling.

Goals

Participants will learn:

Advanced understanding of geostatistics & machine learning models with subsurface workflows in Scikit-learn & TensorFlow on petroleum data sets.

Target Groups

Subject Matter Experts with programming experience in Python

Program Content

Probability.
Data Analytics.
Feature Selection.
Feature Engineering.
Machine Learning.
Clustering.
Advanced Clustering.
Dimensionality Reduction.
Multidimensional Scaling.
Naïve Bayes.
k-Nearest Neighbors.
Decision Tree.
Ensemble Tree.
Support Vector Machines.
Neural Networks.
SHAP.

Code	Date of meeting	Place	Course Price
PS14	28 July - 8 August 2024	USA	$9,900	Register

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Introduction to Subsurface Machine Learning

Introduction

Goals

Target Groups

Program Content

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