In this course, the participants get access to codes and algorithms in python/tensorflow and they apply these software tools on various types of the data. It is a hands-on course that allows participants to learn by assembling various programming modules to design interesting implementations of machine learning.
We’ll look at the realities of a pure machine learning approach through the lens of applied field cases relevant to formation evaluation, reservoir engineering, and production forecasting.
Entry Level and Intermediate
Prerequisites (Knowledge/Experience/Education required)
Basic Computer Programming, Numerical Methods, Statistics, Familiarity with concepts like regression, interpolation, and curve fitting.
The instructor will use Windows OS during the course. Participants will execute python and tensorflow modules/codes to understand various Machine Learning concepts. All software used for the course is open source, so participants should bring computers where they can install the open-source software. Participants need at least 4GB of storage and 4GB RAM on their computer.
- Basics of Machine Learning in Python
- Supervised Learning – Classification
- Case Study #1 – Identifying Rock Type
- Supervised Learning – Regression
- Case Study #2 – Saturation Estimation
- Model Evaluation
- Case Study #3 – Image Analysis and Segmentation
- Cross Validation; Hyper-parameter Selection
- Case Study #4 – Shear Traveltime Prediction
- Unsupervised Learning – Transformation
- Feature Engineering and Feature Selection
- Case Study #5 – Waveform Analysis and Clustering
- Neural Networks
- Participants can perform exploratory data analysis on large datasets containing numerical and categorical data.
- Participants can perform exploratory data analysis on time-series data and unsupervised transformations.
- Participants will be proficient with using Decision Tree Classifiers, kNN classifier, Random forest tree classifier, and K-Means Clustering on various datasets.
- Participants can construct training, testing, cross validation, feature elimination, feature ranking, parameter selection, and anomaly detection tasks.
- Participants can implement advanced clustering, regression, and classification techniques, such as DBSCAN, Hierarchical Clustering, neural networks, ElasticNet, and Support Vector Machines.
- Participants can construct deep neural networks for time-series analysis.