12 November, 2020 Tulsa Oklahoma United States Virtual

Synthetic Gamma Ray Log Generation Using Machine Learning: An End-to-End Upstream E&P Workflow Solution

10-12 November 2020
Virtual Event


Who Should Attend
  • Geoscientists who often hear the buzz around Data Science and are interested in demystifying the same.
  • Geoscientists who are interested in applying the benefits of Data Science to improve their own efficiency.
  • Data Management engineers who would like to understand their role in building Machine learning based solutions.
  • Anybody who hears about Data Science, AI, Machine Learning, Deep Learning and other such buzzwords and would like to have enough confidence to learn and start using these technologies to build practical solutions.

At the end of the course attendees will have:

  1. An understanding of the various aspects of building a machine learning based model and end-user solution.
  2. Broad understanding of the various Machine Learning Methodologies.
  3. Knowledge of using Python and Jupyter notebook to ingest data from the data files, pre-process data, train a Machine Learning Model to synthesize Gamma Rays and visualize results.
  4. Knowledge of the various metrics that can be used to test the performance of a trained Machine Learning model.
Course Content

Course will be held via Zoom: 10–12 November 2020, 6:30 pm–10:00 pm (CDT) 

The course will focus on a Machine Learning workflow in the upstream Oil and Gas domain to generate synthetic Gamma-Ray Logs by applying Artificial Intelligence (AI) Techniques and then learning the various aspects of deploying this workflow in an end-to-end solution that a Geoscientist can use. 

Gamma ray logs are recorded in virtually every oil and gas well drilled. Shales are often more radioactive than reservoir rocks such as sandstone, limestone. The shape of the gamma ray log with respect to depth assists in correlating layers from one well to another. While planning the drilling operation of a new well, synthetic Gamma Ray is very useful in determining Rock Type and formations, so that the Drilling Engineer can plan drilling parameters like RPM, WOB, Mud weight etc. in a better way. This improves the efficiency of the Drilling Operation, creates safe environment and potentially saves huge costs.

The course begins with an introduction to the various phases of designing and implementing a Machine Learning based solution. It then goes into the details of each phase describing the people skills, tools, techniques and methodologies required to complete the tasks successfully. The details of the Synthetic Gamma ray Logs prediction use case are then discussed with the attendees. This is followed by a quick primer on Data Analytics and Python so that the attendees can work on the instructor guided hands-on tutorial to build the machine learning model using python. The next part of the course discusses the various aspects of operationalizing the Machine Learning model as a workflow solution that an end-user can use. The last part of the course presents a real-life case-study including the demo of a complete end-to-end workflow starting from data ingestion and ending with an operationalized solution.

Why This Course Stands Out:

Focus on using Machine Learning to solve real Oil and Gas problems and convert these into end-user centric solutions. This is a comprehensive course covering not only Machine Learning but also the various other aspects of building and deploying a successful ML based solution including Data Ingestion, Pre-processing, Data Lake, Machine Learning and operationalization.

  1. Identify use case and pain points
  2. Identify and collect the relevant data
  3. Quick Introduction to Machine Learning techniques and Python
  4. Build/Train and test the Machine Learning Model using Python
  5. Validation of the model results by Domain Experts
  6. Build solution and operationalize
  7. Demo of a working end-to-end Machine Learning solution
  8. Open Discussion
Expires on
12 November, 2020
Student Fee
Expires on
12 November, 2020
Displaced Professional Fee
30 People
  • Cancellations received on or before 27 October 2020 will be refunded less a $50 processing fee.
  • Refunds will not be issued after 27 October 2020 or for "no shows."
  • You may substitute one participant for another.
  • Cancellations or substitution requests should be emailed to Customer Service at [email protected].
  • Zoom Link (a link will be sent at least 24 hours prior to the course)
  • Digital Course Materials
Technical Requirements
  • Personal Computer
  • High-Speed Internet Connection
  • Search Engine (Google preferred)
AAPG Headquarters
1444 S Boulder Avenue
Tulsa Oklahoma 74119
United States
+1 918 584 2555
Tulsa, OK - AAPG Tulsa, OK - AAPG Virtual 57125 AAPG Headquarters
Accommodation information is not yet available for this event. Please check back often.


Sunil Garg dataVedik, USA
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Events Coordinator +1 918 560-9431
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Director, Innovation and Emerging Science and Technology +1 918 560 2604
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The American Association of Petroleum Geologists (AAPG) does not endorse or recommend any products and services that may be cited, used or discussed in AAPG publications or in presentations at events associated with AAPG.