This course is a hands-on course that will use open-source Python computational platforms, including numpy, sklearn, pandas, and seaborn. This course will provide working knowledge about data analytics and machine learning tools suitable for petroleum engineers, geophysicists, geologists, and geoscientists. In this course, the participants get access to codes and workflows in Python and they apply these pre-built software tools.
Entry Level and Intermediate
Prerequisites (Knowledge/Experience/Education required)
Basic Experience with Programming, Numerical Methods, Basic Statistics, Familiarity with Curve Fitting, Interpolation, and Linear Regression.
Participants will benefit from entry-level content available in the SEG Shop.
The instructor will use Windows OS during the course. Participants will execute pre-built Python and modules/codes to understand various Machine Learning concepts and process various datasets. Participants will not be required to write codes. All software used for the course is open source, so participants should bring computers where they can install the open-source software and codes. Participants need at least 4GB of storage and 4GB RAM on their computer.
- Assemble open-source machine learning and data mining workflows in Python to solve data-driven problems related to petroleum engineering and petroleum geosciences.
- Participants will use Elastic Net, Neural Networks, Nearest Neighbor, Random forest, and K-Means.
- Participants will be proficient in cross validation, hyperparameter optimization, feature transformation and model evaluation tasks.
- Case Study #1 – Exploratory data analysis on time-series data
- Case Study #2 – Data Preprocessing and Outlier Detection
- Case Study #3 – Rock Facies Classification
- Case Study #4 – Rock Typing using Clustering Methods
- Case Study #5 – Data-Driven Geomechanical Characterization