Machine Learning Techniques for Engineering and Characterization

by Siddharth Misra

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.

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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.

Intended Audience

Entry Level and Intermediate

Prerequisites (Knowledge/Experience/Education required)

Basic Computer Programming, Numerical Methods, Statistics, Familiarity with concepts like regression, interpolation, and curve fitting.

Equipment/Software Requirements

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.

Course Outline

  1. Basics of Machine Learning in Python
  2. Supervised Learning – Classification
  3. Case Study #1 – Identifying Rock Type
  4. Supervised Learning – Regression
  5. Case Study #2 – Saturation Estimation
  6. Model Evaluation
  7. Case Study #3 – Image Analysis and Segmentation
  8. Cross Validation; Hyper-parameter Selection
  9. Case Study #4 – Shear Traveltime Prediction
  10. Unsupervised Learning – Transformation
  11. Feature Engineering and Feature Selection
  12. Case Study #5 – Waveform Analysis and Clustering 
  13. Neural Networks

Learner Outcomes

  • 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.

Instructor Bio

Siddharth Misra

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