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.

This course will be offered at the NAPE Summit 3–4 February 2020.

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This course will be offered at the SEG-GSH Education Week 24–27 March 2020.

Register now

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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.

Duration

Two days

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. Exploratory Data Analysis
  2. Supervised Learning - Regression and Classification
  3. Unsupervised Transformations
  4. Unsupervised Learning - Clustering
  5. Feature Extraction and Feature Ranking
  6. Anomaly Detection
  7. Cross Validation and Model Scores
  8. Parameter Selection
  9. Advanced Regression, Classification, and Clustering
  10. Neural Network
  11. TensorFlow and Deep Neural Networks
  12. Machine Learning for Time Series Data
  13. Hands on Petroleum Case Study 1: Applications of Shallow Learning Methods
  14. Hands on Petroleum Case Study 2: Applications of Deep Neural Network
  15. Hands on Petroleum Case Study 3: Applications of Data Mining
  16. Interesting Use Cases of Machine Learning in Upstream Oil and Gas

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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