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EXECUTIVE SUMMARY

Energy professionals – current and future – have to gain deeper insights into operational data for making tougher decisions. UH Energy’s new Upstream Energy Data Analytics Program answers this need for the upstream oil and gas industry.

Designed and presented by leaders from industry and accomplished faculty from the University of Houston, the program provides a structured series of micro-credentials or “badges” that will provide the necessary data sciences skillset to facilitate developing solutions to current and emerging challenges using advanced data-based decision making.

Each badge is a 15-hour module, delivered over a 3-week period, and the badges are stackable. The first three badges, which together form the Bronze Belt in Upstream Energy Data Analytics, are available online from UH Energy.

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PROGRAM OVERVIEW

The oil and gas industry is undergoing significant changes, including repositioning operations to be cost-competitive in a world of low oil prices, and meeting the growing demands of energy in a sustainable way.

This requires working in smarter and more efficient ways. Innovation has always been at the core of the oil and gas industry. Many oil and gas companies are already finding ways to implement data sciences solutions and are realizing tangible benefits, including competitive advantage.

Why This Program?

Data sciences are already playing an important role in addressing several industry challenges. Industry leaders have validated these claims and have recognized that there is a workforce shortage in skilled data sciences with an understanding of the energy industry.

For this reason UH Energy, at the University of Houston, has developed the Upstream Energy Data Analytics Program, to equip current and aspiring professionals in the upstream oil and gas industry on data analytics concepts and hands on experience on applications with real world examples.

UH Energy will deliver the program in collaboration with NExT, a Schlumberger Company, UH Departments of Earth & Atmospheric Sciences and Petroleum Engineering and the HPE Data Sciences Institute at UH.

Who Should Attend?

The Upstream Energy Data Analytics Program has been designed with two distinct groups in mind:

  • Those of you who are already in the upstream industry and facing challenges daily in making operations more efficient while continuing to grow the business. The Upstream Energy Data Analytics program will enhance your capabilities to get deeper insights from the data that you deal with, facilitating finding solutions to above challenges.
  • If you are readying yourself for an upstream career, you need to prepare yourself for a dynamic, exciting and challenging professional life. The Upstream Energy Data Analytics program provides a unique perspective and a ready to apply practical skillset, giving you a competitively advantaged competence as you enter the upstream oil and gas marketplace.

Pricing

INDIVIDUAL BADGE PRICE:

$900

EACH

BRONZE OR SILVER BELT BUNDLE (All 3 Badges purchased together):

$2,500

ALL 3

Schedule

UH Energy is offering the following Bronze Badge sessions in Upstream Energy Data Analytics. Instruction for each badge consists of 6, 2.5-hour online sessions. Each student should allow for 1.5 hrs. each for midterm and final test. Sessions are delivered two times per week, Monday and Thursday, from 6:00pm-8:30pm, US Central Time.

FALL BRONZE BADGES

Current dates for the three badges are:

Data Processing and Machine Learning Badge: SEPTEMBER 13 SEPTEMBER 16 SEPTEMBER 20 SEPTEMBER 23 SEPTEMBER 27 SEPTEMBER 30

Model Evaluation and Clustering Badge: OCTOBER 4 OCTOBER 7 OCTOBER 11 OCTOBER 14 OCTOBER 18 OCTOBER 21

Alternate Machine Learning Algorithms Badge: NOVEMBER 1 NOVEMBER 4 NOVEMBER 8 NOVEMBER 11 NOVEMBER 15 NOVEMBER 18

*Though not required, it is strongly recommended to take the badges in the above specified sequence, as the later badges build on the content of earlier badges.

Credentialing Overview

The course is offered in 15-hour modules, each over a 3-week period. Digital badges are awarded for each module. For completing each group of 3 Badges, learners will earn a Belt. There are three belts in the program. The Introductory Belt (Bronze) is followed by an Intermediate Belt (Silver) and finally by an Advanced (Gold) Belt. The Belts and Badges will be a permanent addition to your skillset and resume.

BRONZE BELT

The Bronze Belt provides the participants the tools and techniques to build and evaluate data-driven models via the machine learning approach. It covers the data analytics techniques to extract knowledge from raw data by building data-driven models. All aspects of data-mining – data exploration, data preprocessing, machine learning modeling, and model evaluation – are covered. The sessions combine theoretical knowledge with hands-on training of the data analytics techniques using real oil and gas datasets.

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Data Processing and Machine Learning Badge 1 Image
Model Evaluation and Clustering Badge 2 Image
Alternate Machine Learning Algorithms Badge 3 Image

SILVER BELT

Python is an easy to learn, powerful programming language. It has efficient high-level data structures that make it suitable for rapid application development. Topics covered in this session will include data types, conditional & loop statements, functions, input/output, modules and regular expressions. Upon completion of this course, participants should be able to understand existing scientific python codes as well as write their own python applications.

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Introduction to Python Badge 4 Image
Python Data Analysis Badge 5 Image
Text Mining Badge 6 Image

GOLD BELT

The Gold Belt will introduce participants to tools and techniques for building and interpreting valid models for time series data using examples in the upstream oil and gas industry. Participants will also be introduced to fundamentals of Deep Learning, as well as Convolutional Neural Networks and Recurrent Neural Networks. Hands on sessions will focus on building CNN and RNN using the Keras library in Python.

Learning Objectives

After successfully completing the Introductory (Bronze) Belt, participants will understand how to extract knowledge from raw data and be skilled at:

  • Data Exploration
  • Data Preprocessing
  • Machine Learning Modeling
  • Evaluating Model Performance
  • Improving Model Performance

Credentialing Program Prerequisites

Either:

  • Rising senior in a bachelor’s degree program in engineering, technology or business with an understanding of upstream oil and gas operations such as seismic, drilling and production
  • Industry Professional

Frequently Asked Questions

The first step is to complete the
UH Energy will review your application to ensure that you satisfy the minimum requirements to register. For initial Badges, this means that you should either be:
  • An Industry Professional - Priority is given to applicants from within the energy sector, with experience in upstream oil and gas operations
  • A Student - Either in a graduate program, or a rising senior in a bachelor’s degree program in engineering, technology or business with an understanding of upstream oil and gas operations such as seismic, drilling and production
After we received your application, it will be reviewed by the admissions committee. They will notify you of their decision within two business days. If we are able to offer you a seat in the program, we will send you instructions for online payment. Your application will be accepted upon receipt of your payment.
Currently we only accept credit card payments.
The course is delivered online as a live synchronous delivery. Some of the lectures are prerecorded in segments of roughly 20 minutes each. The recorded segments are followed by live interaction with instructors in real time. The live interaction will be used to expand on some of the topics in the recorded lectures, and to resolve any misunderstandings and/or questions that that learners may have. Numerical examples will be solved using Orange software to illustrate the principles taught and students will have an opportunity practice solving problems using Orange software.
Yes. Each Badge is covered over two weeks. At the end of the first week, students will have an online multiple choice test for homework, and will have to achieve a passing grade to proceed to the next week. If one does not pass on the first attempt, one can review the course material and re-take the test as many times as one wishes. At the end of the second week, there will be a final exam for the Badge. One needs to get a passing grade to move on to the next Badge. The same rules for passing apply for the final as for the test held at the end of the first week.
If you miss a class, or a portion of a badge, the material will be available as recorded material, and you can view the recordings at your convenience. However, we strongly recommend that you attend the course synchronously, as the live interaction with instructors is an important instructional component.
No. However, the later badges build on the content of the earlier ones, so it is much preferred to take the badges in the specified sequence.
If you miss a badge, you may be able to register for the badge for delivery through asynchronous recordings, and you will have to complete and pass all of the homework and exams to earn the badge. The asynchronous (i.e., recorded) material will be available, and you can qualify by viewing the recordings and achieving passing grades on the tests. However, we strongly recommend that you attend the course synchronously, as the live interaction with instructors is an important instructional component. The same registration process and fees apply whether you participate in the course in real time, or if you only participate asynchronously.

Micro-credentials are certifications for mastery of specific topic areas or skillsets. To earn a micro-credential, you typically have to complete a certain number of activities, assessments, or projects related to the topic.

Digital badges (or ebadges) are a validated indicator of accomplishment, skill, quality or interest that can be earned in various learning environments. Digital badges are now commonly used as “digital transcripts,” and they can be incorporated in LinkedIn profiles. Many badge earners also display their badges through social media.

Use and Acceptance: Micro-credentials and digital badges are now widely accepted by employers as evidence of competency in specific skills. They have been in use for roughly ten years, notably in the IT arena, where IBM has been a leader. IBM needed to train personnel to support rapidly changing product needs. Rather than using extended training programs, they developed training for minimum skillsets, and deployed this through online media. They awarded micro-credentials to successful candidates and issued digital badges to certify the awards. Many other companies, large and small, are now using digital credentials, as are many academic institutions, including the University of Houston, Harvard, Cambridge, and a large number of other universities.

Students who have registered can withdraw from the course at any time. However, participants need to withdraw two days before start of classes to receive a refund. If you have paid for the Bronze bundle (all three badges) and would like to withdraw after completing a badge, you may do so two days before the start of classes for the next badge to receive a refund for the remaining badges.

If you cannot find the information you need on the webpage or in the other FAQs, please contact:

Helpful Program Assets

READ PAPER

Abstract: The upstream oil and gas industry's digital transformation over the last few years has accelerated because of the COVID-19 pandemic. Data analytics and machine learning are key components of this digital transformation and have become essential skills for experienced petrotechnical professionals (PTPs) and aspiring entrants into the field. The objective of our work was to design and deliver a practical, engaging, and online microcredential certification program in upstream energy data analytics for PTPs.

The program was conceived as a collaboration between academia (University of Houston's UH Energy) and industry (NExT, a Schlumberger company). It was designed as three belt levels (Bronze, Silver, and Gold), each containing three stackable badges of 12 to 15 hours duration per badge. Key design points included:

  1. Identifying an online platform for administration
  2. Delivering convenient, interactive, live online sessions
  3. Delivering hybrid classes blending lectures and hands-on laboratories
  4. Designing laboratories using upstream datasets across various stages of oilfield expertise
  5. Administering test and quizzes, Kaggle competitions, and team projects

The program contents were designed incorporating appropriate instructional design practices for effective online class delivery. The design and delivery of the laboratories using a code-free approach by leveraging visual programming offers PTPs and new entrants a unique opportunity to learn data analytics concepts without the traditional concern of learning to code. Additionally, the collaboration between academia and industry enables delivering a program that combines academic rigor with application of the skills and knowledge to solve problems facing the industry using the real-world datasets.

As a pilot program, all three badges of the Bronze belt were scheduled and successfully delivered during July and August 2020, as six 2-hour sessions per badge. From a total of 26 students registered in badge 1, 24 completed it, resulting in a completion rate of 92%. Out of these students, 19 registered and completed badge 2 and badge 3, resulting in the completion rates of 100%. Based on the success of the pilot program, a second delivery of the Bronze belt with 18 participants was offered from October 2020 through January 2021. All 18 participants completed all three badges. Feedback from participants attests to the success of the pilot program as seen in the following excerpts:

  • "A very good course and instructors. I have already recommended the course to a friend and I will continue to be an advocate for the course."
  • "Teachers are very receptive to questions and it is a joy to hear their lectures."
  • "I found the University of Houston course to be both highly engaging and incredibly informative. The course teaches basic principles of data science without being bogged down by the specific coding language."

Testimonials