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SC01 Machine Learning Techniques for Engineering and Characterization

Sponsored by: SEG

Friday, 17 June Saturday, 18 June 2022, 8:00 a.m.–5:00 p.m.  |  Houston, Texas

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Who Should Attend

Entry Level and Intermediate Prerequisites (Knowledge/Experience/Education required) Basic Computer Programming, Numerical Methods, Statistics, Familiarity with concepts like regression, interpolation, and curve fitting.

Objectives

  • 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.

Course Content

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
 

  • 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

Fees

Pricing:
Members - $500
Non-Members - $600
Students - $250
Room Assignment:
Online at Zoom
Attendee Limit:
16 People
Education Credits:
1.6 CEU

Venue

SC01 Machine Learning Techniques for Engineering and Characterization
George R. Brown Convention Center
1001 Avenida De Las Americas
Houston, Texas 77010
United States
(713) 853-8000

Instructor

Siddharth Misra
Siddharth Misra Texas A&M University
Prof. Siddharth Misra is an Associate Professor in Harold Vance Department of Petroleum Engineering at Texas A&M University. Misra holds a Ph.D. in Petroleum Engineering from The University of Texas at Austin. Prior to that, from 2007 to 2010, he worked as a Wireline Field Engineer in Saudi Arabia, Egypt, and USA with Halliburton. He received his undergraduate degree in Electrical Engineering from Indian Institute of Technology Bombay, India, in 2007. Recently, he was awarded the prestigious Department of Energy Early Career Award, American Chemical Society New Investigator Award, SPE Mid-Continent Formation Evaluation Award, SEG Clarence Karcher Award, and the SPWLA Young Professional Technical Award. His research interests include subsurface characterization, machine learning, sensing and sensors, and inverse problems.