Machine Learning for Subsurface Engineering Professionals
Workshop Overview
This course will provide working knowledge on using open-source packages essential for data analytics and machine learning. The entire course is based on live demos of workflows. The course will help geoscientists, geophysicists, and petroleum engineers learn applied E&P analytics at a beginner to intermediate level. The course uses various types of data: well logs, core data, well performance data, waveform data, images, and production data.
The focus of this course is on introducing skills that are pre-requisites to real-world data analysis. The course will not explore applications on large-sized field data. The group project at the end of the course will help the participants try out the learned concepts in hands-on manner. The practice session will allow deeper interaction with the instructor on problems relevant to the participants
Workshop Outline
Benefit of Attending this Workshop
- ASSEMBLE open-source coding and scripting workflows in Python to solve basic data science problems related to subsurface data.
- APPLY numpy, pandas, matplotlib, seaborn and sklearn packages on subsurface data.
- SOLVE supervised regression problems using ElasticNet, random forest, nearest neighbor, and LASSO regressors.
- EXPLOIT supervised classification problems using nearest neighbor, random forest, and support vector classifiers.
- DETERMINE unsupervised clustering problems using k-means and mean shift techniques.
- EXPLOIT anomaly detection and data preprocessing.
- LEARN neural network and boosting methods.
Who Should Attend to This Workshop
This course is intended for Energy industry professionals (petroleum engineers, geoscientists, geophysicists, geologists, to name a new).
Course Date
Testimonials
Course Info
- Claimable : Claimable
- Venue : Kuala Lumpur, Malaysia
- Certificate of Competence Included
- Customization: Fully-Customised