Bronze Badge: Data Processing & Machine Learning
Badge holders explored one of the four data analytics modules called exploratory data analysis, a necessary step to clean and format data before building machine learning models. Supervised machine learning concepts were introduced, and participants built data-driven models via supervised machine learning algorithms. Two machine learning algorithms - kNN and Decision Tree Classifier were covered in this badge. Course Details: Exploratory Data Analysis and Data Preprocessing: Introduction to Data Analytics • Exploratory Data Analysis (EDA) (Visualization, and Descriptive Statistics) • Data Preprocessing (including Principal Component Analysis) Supervised Machine Learning: kNN • Decision Tree Classifier. 12 PDH/1.2 CEU
Student’s understanding of concepts and ability to apply the principles to real world data sets are evaluated thru in class quizzes, lab work in class, homework and exams. The scores are weighted as follows: Labs 10% Homework 40% Mid-term Exam 25% Final Exam 25% The course uses the PASS / FAIL grading system. Average course score 80 and more will result in the PASS grade.
Bronze Badge: Model Evaluation & Clustering
Participants used Model evaluation matrices (e.g., confusion matrix, ROC, AUC, etc.) and model evaluation methods (e.g., 10-fold cross validation) and improved model performance through automatic tuning of model parameters. Participants completed a hands-on session on Model Evaluation and were introduced to more advanced machine learning techniques of ensemble methods. Participants learned to implement the complete data mining pipeline including model building and model evaluation in Orange. Participants were introduced to unsupervised machine learning algorithms for cluster analysis. Course Details: Evaluating and Improving Model Performance • Evaluating model performance • Improving model performance Ensemble Methods: Ensemble Methods (Bagging, Boosting and Random Forest) Unsupervised Machine Learning: Cluster Analysis (k-Means and Hierarchical). 12 PDH/1.2 CEU
Bronze Badge: Alternate Machine Learning Algorithms
Participants used 5 other popular supervised machine learning algorithms and learned concepts behind these algorithms and completed hands-on labs using data set. Participants learned how these algorithms are used for tackling classification and regression problems through hands-on labs. Participants also used Self Organizing Maps, a type of ANN that uses unsupervised machine learning for dimensionality reduction. Course Details: Use of other popular Machine Learning algorithms: Linear Regression • Logistic Regression • Naïve Bayes (Probabilistic Learning) • Support Vector Machines (SVM) • Artificial Neural Network (ANN) • Self Organizing Maps (SOM). 12 PDH/1.2 CEU
This badge provides the foundation to Belt 1 of Sustainable Energy Development (SED) Program. Learners will gain an understanding of the fundamental challenge in SED – the Energy Trilemma; ability to meet the growing energy needs reliably, with a cost structure that is economically competitive and environmentally responsible. The badge provides a background of the energy industry – the suppliers, the customers, the global supply demand challenges, the environmental issues in different sectors of the industry; having an understanding of which is important in making decision on addressing the Trilemma. The badge also covers through industry case studies key aspects of sustainability - deciding on what to measure and how to communicate progress – a key component to track one’s progress in addressing the Trilemma. Credit: 15 PDH/1.5 CEU
• Complete 15 hours of online instruction • Complete homework exercises • Achieve a passing grade of at least 75% in qualifying tests
This badge examines the pathways for a sustainable transition away from fossil fuels. Energy transitions are not new: the world has undergone several big shifts in its dominant energy sources. We first look at this history and the lessons it offers. Then, we talk about coal: the fuel which industrialized and continues to industrialize the world, but at a great societal and environmental cost. The focus then shifts to oil and natural gas, which are central to our current energy supply, but face numerous issues of their own. Some alternatives to them—nuclear and hydroelectric energy—are up and running, but not on a large enough scale to displace them. Others—wind, solar, and biomass—are growing rapidly from a low base. The class concludes with a critical look at our energy consumption patterns. Credit: 15 PDH/1.5 CEU
Complete 15 hours of online instruction • Complete homework exercises • Achieve a passing grade of at least 75% in qualifying tests
Bronze Badge: Evaluating Sustainable Energy Options
This badge introduces key dimensions for evaluating sustainable energy options – factors to consider, information required, and a framework to use. It also provides instruction in some tools to use in the evaluation process; and it includes breakout sessions to reinforce concepts and for learners to practice the techniques that have taught. Specific topics covered: • Techno-economic analysis • Life cycle analysis • Competitive landscape • Business risk • Public policy & government action • Reporting & investor relations Credit: 15 PDH/1.5 CEU
Sustainable Energy Development: Bronze Belt
In order to earn the Sustainable Energy Development Bronze Belt, students had to complete 3 bronze badges: Energy Transition: Learners gained an understanding of the fundamental challenge in SED – the Energy Trilemma; ability to meet the growing energy needs reliably, with a cost structure that is economically competitive and environmentally responsible. The badge provides a background of the energy industry – the suppliers, the customers, the global supply demand challenges, the environmental issues in different sectors of the industry; having an understanding of which is important in making decision on addressing the Trilemma. The badge also covers through industry case studies key aspects of sustainability - deciding on what to measure and how to communicate progress – a key component to track one’s progress in addressing the Trilemma. Energy Value Chain: This badge examines the pathways for a sustainable transition away from fossil fuels. Energy transitions are not new: the world has undergone several big shifts in its dominant energy sources. We first look at this history and the lessons it offers. Then, we talk about coal: the fuel which industrialized and continues to industrialize the world, but at a great societal and environmental cost. The focus then shifts to oil and natural gas, which are central to our current energy supply, but face numerous issues of their own. Some alternatives to them—nuclear and hydroelectric energy—are up and running, but not on a large enough scale to displace them. Others—wind, solar, and biomass—are growing rapidly from a low base. The class concludes with a critical look at our energy consumption patterns. Evaluating Sustainable Energy Options: This badge introduces key dimensions for evaluating sustainable energy options – factors to consider, information required, and a framework to use. It also provides instruction in some tools to use in the evaluation process; and it includes breakout sessions to reinforce concepts and for learners to practice the techniques that have taught. Specific topics covered: • Techno-economic analysis • Life cycle analysis • Competitive landscape • Business risk • Public policy & government action • Reporting & investor relations Credit: 45 PDH/4.5 CEU
• Complete 45 hours of online instruction • Complete homework exercises • Achieve a passing grade of at least 75% in qualifying tests
Upstream Energy Data Analytics: Bronze Belt
In order to earn the Upstream Energy Data Analytics Bronze Belt, students had to complete 3 bronze badges: Data Processing & Machine Learning: Badge holders explored one of the four data analytics modules called exploratory data analysis, a necessary step to clean and format data before building machine learning models. Supervised machine learning concepts were introduced, and participants built data-driven models via supervised machine learning algorithms. Two machine learning algorithms - kNN and Decision Tree Classifier were covered in this badge. Model Evaluation & Clustering: Participants used Model evaluation matrices (e.g., confusion matrix, ROC, AUC, etc.) and model evaluation methods (e.g., 10-fold cross validation) and improved model performance through automatic tuning of model parameters. Participants completed a hands-on session on Model Evaluation and were introduced to more advanced machine learning techniques of ensemble methods. Participants learned to implement the complete data mining pipeline including model building and model evaluation in Orange. Participants were introduced to unsupervised machine learning algorithms for cluster analysis. Alternate Machine Learning Algorithms: Participants used 5 other popular supervised machine learning algorithms and learned concepts behind these algorithms and completed hands-on labs using data set. Participants learned how these algorithms are used for tackling classification and regression problems through hands-on labs. Participants also used Self Organizing Maps, a type of ANN that uses unsupervised machine learning for dimensionality reduction. 36 PDH/3.6 CEU
• Complete 36 hours of online instruction • Complete homework exercises • Achieve a passing grade of at least 80% in qualifying tests
The world is undergoing an energy transition with a focus on providing a growing global population with affordable, reliable and sustainable energy. This is placing increasing importance on how energy businesses’ address their ESG performance. Participants in this badge received training in developing a framework for managing ESG in their organization, and a roadmap for its implementation. This includes: • Gaining an understanding of the various dimensions of ESG • Learning how energy companies in different segments of the industry approach ESG. Areas of focus: Reporting of ESG performance; Integration of ESG into company operations; Skills and tools required to manage ESG; • Industry practitioners and subject matter experts will share their knowledge through case studies on differing levels of engagement in ESG. • Participants worked through examples and case studies. • Utilizing an ESG Framework participants developed the ESG plan for their company.
Complete 12 hours of online instruction • Complete homework and case study exercises • Achieve a passing grade of at least 80% in homework, case studies and exams
Building trusting relationships with key business stakeholders is a key cornerstone of any successful ESG/Business program. This badge covers aspects of how to build, maintain and manage stakeholder relationships in the energy industry. This includes: Identifying key internal and external stakeholders; Assessment of stakeholder needs; Development of stakeholder strategies and their implementation including engagement, communication, monitoring & reviewing of progress and impact; Development of engagement capabilities of operations and other staff interfacing with stakeholders. Develop a companywide stakeholder management process for ESG Industry practitioners and subject matter experts will share their knowledge about Stakeholder Management through case studies. Participants will work through examples and case studies to develop their Stakeholder Management capabilities through established frameworks and strategies.
Managing Risk is an important and integral part of managing any energy business. Entities track the major risks to the business and report on actions taken to manage them as part of their Annual Report. Participants in this badge will develop a comprehensive understanding, of how to build a risk management process as it pertains to ESG programs in the energy industry and they will learn tools and techniques to facilitate this process. Participants will learn how to: Identify ESG risks in the energy industry; Establish Frameworks for assessment of ESG risk; Develop management plans for ESG Risk; Incorporate ESG risks assessment into business and company risk and provide monitoring and evaluation. Industry practitioners and subject matter experts will share their knowledge about ESG risk through case studies. Participants will work through examples and case studies using ESG Risk Management tools.
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 are able to understand existing scientific python codes as well as write their own python applications.
Python Data Analysis covers packages such as NumPy, Pandas and concepts of Data Wrangling, Plotting and Visualization and Data Aggregation. Modeling libraries such as Patsy, statsmodels and scikit-learn are taught. Participants learned how to train and evaluate machine learning models using Python and Jupyter Notebook as the Integrated Development Environment. Hands-on sessions are based on public upstream oil and gas datasets.
Participants were introduced to text mining and learned how to derive actionable insights from unstructured text data. Participants will get exposed text preprocessing techniques such as tokenization, stop word removal, stemming, lemmatization and parsing text data. They also learned feature engineering concepts for representing text data such as Bag of Words model, Bag of N-Grams model and TF-IDF model. Text classification and text clustering was also covered and hands on sessions using Python libraries such as NLTK and/or Orange helped participants gain a practical understanding of text mining.
Upstream Energy Data Analytics: Silver Belt
Building on the learnings of the Bronze Belt, the Silver Belt is designed to provide the foundations of Python programming for developing powerful and reusable data analysis models and Text Analytics. The students will get hands-on training on writing programs to facilitate discoveries from data. The topics include data import/export, data types, control statements, functions, basic data processing, and data visualization. Participants in the Silver Belt must have completed Bronze Badges 1 through 3 to participate or demonstrate competency in previous topics.
The energy transition is in progress, and hydrogen is a key component of most credible sustainable energy scenarios, with up to 10-fold growth projected for a global energy system that will increase less than two-fold over the next 30 - 50 years. Topics covered in this badge program include: factors affecting current interest in hydrogen; hydrogen energy basics; hydrogen in the energy transition; and current and emerging production methods for hydrogen. Upon completion of this course, participants are able to understand the technology, economics, policy, and business drivers and barriers for hydrogen production.
Students’ understanding of concepts and ability to apply the principles to real world situations are evaluated through weekly tests and a final exam. The scores are weighted as follows: Weekly Tests 66⅔% | Final Exam 33⅓% The course uses the PASS / FAIL grading system. Average course score 80 and more will result in the PASS grade.
Silver Badge 2: Hydrogen Transportation, Safety & Use
Topics covered in this badge program include: hazards associated with the production, distribution and use of hydrogen, and their mitigation; differences in handling for gaseous and liquid hydrogen; options for transportation and storage of hydrogen; uses of hydrogen in energy storage and decarbonization applications; and regional strategies for deploying hydrogen. Upon completion of this course, participants are able to understand the basics of hydrogen safety; technical options for storing and transporting hydrogen, including decision factors; and the use of hydrogen as a means for transporting and storing renewable energy.
Silver Badge 3: Commercial Opportunity for Hydrogen
Topics covered in this badge program include: hydrogen in the energy transition; hydrogen in solar fuels and chemicals, including ammonia; hydrogen applications in mobility (light and heavy duty road transportation, including buses; trains, shipping, and airplanes); grid store and data centers. A key part of the program is the capstone project, in which students apply concepts presented in the course to develop a business case for hydrogen deployment in a chosen application. Upon completion of this course, participants are able to understand the key roles for hydrogen as they relate to the current energy transition; the cases for hydrogen fueled vehicles – from forklifts to aircraft; and fuel cells and their roles in transportation, in the electric grid, and in domestic and commercial power supply.
Students’ understanding of concepts and ability to apply the principles to real world situations are evaluated through a capstone project, in which they apply concepts presented in the course to develop a business case for hydrogen deployment in a chosen application. Students must present their business case verbally, and deliver supporting written documentation. The scores are weighted as follows: Capstone Project: 100% The course uses the PASS / FAIL grading system. A capstone project score of 80% or more will result in the PASS grade.
In order to earn The Hydrogen Economy Silver Belt, students had to complete 3 silver badges: Badge 1. Hydrogen Production: The energy transition is in progress, and hydrogen is a key component of most credible sustainable energy scenarios, with up to 10-fold growth projected for a global energy system that will increase less than two-fold over the next 30 - 50 years. Topics covered in this badge program include: factors affecting current interest in hydrogen; hydrogen energy basics; hydrogen in the energy transition; and current and emerging production methods for hydrogen. Upon completion of this course, participants are able to understand the technology, economics, policy, and business drivers and barriers for hydrogen production. Badge 2. Hydrogen Transportation, Safety & Use: Topics covered in this badge program include: hazards associated with the production, distribution and use of hydrogen, and their mitigation; differences in handling for gaseous and liquid hydrogen; options for transportation and storage of hydrogen; uses of hydrogen in energy storage and decarbonization applications; and regional strategies for deploying hydrogen. Upon completion of this course, participants are able to understand the basics of hydrogen safety; technical options for storing and transporting hydrogen, including decision factors; and the use of hydrogen as a means for transporting and storing renewable energy. Badge 3. Commercial Opportunity for Hydrogen: Topics covered in this badge program include: hydrogen in the energy transition; hydrogen in solar fuels and chemicals, including ammonia; hydrogen applications in mobility (light and heavy duty road transportation, including buses; trains, shipping, and airplanes); grid store and data centers. A key part of the program is the capstone project, in which students apply concepts presented in the course to develop a business case for hydrogen deployment in a chosen application. Upon completion of this course, participants are able to understand the key roles for hydrogen as they relate to the current energy transition; the cases for hydrogen fueled vehicles – from forklifts to aircraft; and fuel cells and their roles in transportation, in the electric grid, and in domestic and commercial power supply.
• Complete 45 hours of online instruction • Achieve a passing grade of at least 75% in qualifying tests • Complete the capstone project, applying concepts presented in the course to develop a business case for hydrogen deployment in a chosen application.
Bronze Badge 1: Exploratory Data Analysis & Data Visualization
Data Analytics for the Process Industries is designed to demystify the field for industry professionals and students, and to show how statistical concepts and tools can be used solve practical problems within the process industries. Topics covered in Badge 1 include: static and dynamic models, visualization and descriptive statistics, data preparation (outliers, centering, scaling, detrending, missing data, correlated data, other), plotting, core statistical facts, basic rules of thumb. Applications at MS Excel-level. Upon completion of this course, participants are able to understand the basic concepts of statistical analysis, and how MS Excel can be used for a range of statistical applications in oil refining and chemicals. Course Credit: 15 PDH/1.5 CEU
Complete 15 hours of online instruction. Students’ understanding of concepts and ability to apply the principles to real world situations are evaluated by in-class quizzes, homework, and a final exam. The scores are weighted as follows: Quizzes & Homework: 20%|Final Exam 80% The course uses the PASS / FAIL grading system. Average course score 70 and more will result in the PASS grade.
Bronze Badge 2: Programming Basics and Solving Practical Problems
Data Analytics for the Process Industries is designed to demystify the field for industry professionals and students, and to show how statistical concepts and tools can be used solve practical problems within the process industries. Topics covered in Badge 2 include: introduction to arrays and structures, looping and conditionals, functions and scripts, debugging and error handling, and additional ways of visualizing and plotting data. The course also includes an introduction MATLAB as tool for conducting data analysis. Upon completion of this badge, participants are able to understand the analytical concepts listed above, and how MATLAB can be used for a range of applications in oil refining and chemicals. Course Credit: 15 PDH/1.5 CEU
Complete 15 hours of online instruction. Students’ understanding of concepts and ability to apply the principles to real world situations are evaluated by in-class quizzes, homework, and a final exam. The scores are weighted as follows: Quizzes & Homework: 20% |Final Exam 80% The course uses the PASS / FAIL grading system. Average course score 70 and more will result in the PASS grade.
Bronze Badge 3: Dynamic Models, Design of Experiments, and Classification
Data Analytics for the Process Industries is designed to demystify the field for industry professionals and students, and to show how statistical concepts and tools can be used solve practical problems within the process industries. Topics covered in Badge 3 include: • Dynamic models: A hands-on approach • Building dynamic models from data: Practice and a basic theory • Designing experiments for generation of good-quality data • Analysis of dynamic operating data • Introduction to classification • Overview of multivariate methods in the context of regression (principal component regression) and classification (principal component analysis) Upon completion of this badge, participants are able to understand the analytical concepts listed above, and apply them to a range of applications in oil refining and chemicals. Course Credit: 15 PDH/1.5 CEU
1. Complete 15 hours of online instruction. 2. Students’ understanding of concepts and ability to apply the principles to real world situations are evaluated by in-class quizzes, homework, and a final exam. The scores are weighted as follows: Quizzes & Homework: 20% | Final Exam 80% The course uses the PASS / FAIL grading system. Average course score 70 and more will result in the PASS grade.
Data Analytics for the Process Industries: Bronze Belt
The Data Analytics for the Process Industries Bronze Belt is designed to demystify the field for industry professionals and students, and to show how statistical concepts and tools can be used solve practical problems within the process industries. In order to earn the Data Analytics for the Process Industries Bronze Belt, students had to complete 3 badges: Badge 1. Exploratory Data Analysis & Data Visualization: Topics covered in Badge 1 include static and dynamic models, visualization and descriptive statistics, data preparation (outliers, centering, scaling, detrending, missing data, correlated data, other), plotting, core statistical facts, basic rules of thumb. Applications at MS Excel-level. Upon completion of this course, participants are able to understand the basic concepts of statistical analysis, and how MS Excel can be used for a range of statistical applications in oil refining and chemicals. Badge 2. Programming Basics and Solving Practical Problems: Topics covered in Badge 2 include introduction to arrays and structures, looping and conditionals, functions and scripts, debugging and error handling, and additional ways of visualizing and plotting data. The badge also includes an introduction MATLAB as tool for conducting data analysis. Upon completion of this badge, participants are able to understand the analytical concepts listed above, and how MATLAB can be used for a range of applications in oil refining and chemicals. Badge 3. Commercial Opportunity for Hydrogen: Topics covered in Badge 3 include - • Dynamic models: A hands-on approach • Building dynamic models from data: Practice and a basic theory • Designing experiments for generation of good-quality data • Analysis of dynamic operating data • Introduction to classification • Overview of multivariate methods in the context of regression (principal component regression) and classification (principal component analysis) Upon completion of this badge, participants are able to understand the analytical concepts listed above, and apply them to a range of applications in oil refining and chemicals. Combined Credit: 45 PDH/4.5 CEU
1. Complete 45 hours of online instruction, covering Badges 1, 2 & 3 of the program 2. Students’ understanding of concepts and ability to apply the principles to real world situations are evaluated by in-class quizzes, homework, and a final exam. The scores are weighted as follows: Quizzes & Homework: 20%|Final Exam 80% The program uses the PASS / FAIL grading system. Average course score 70 and more will result in the PASS grade.
GEOL 7323 Badge 1: Modeling Rock Properties
Understanding what is meant by a model and how models are built is fundamental to evaluating rock formations for hydrocarbon potential. Models are highly dependent on the lithology and petrophysics of source rocks and reservoir rocks, including porosity and permeability. Topics covered in this badge include traditional reservoir rock and fluid properties and concludes with a review of saturation height modeling. Upon completion of this course, students will understand how and why modeling is important for evaluating formations and how to build a saturation height model based on rock properties.
1. 15 hours of instruction, face to face or online 2. Completion of graded assignment on saturation height modeling
Well logging is a vital component in evaluating the hydrocarbon potential of rocks because it is the first physical evidence of the rocks penetrated by a well. Topics covered in this badge include measuring and interpreting properties such as porosity, resistivity, SP, gamma, and other reservoir indicators and how to compare and interpret properties using cross-plots. Quick scanning techniques and Rwa calculations are also discussed. Upon completion of this course, students will be able to interpret physical property logs of wells and evaluate their hydrocarbon potential.
1. 15 hours of instruction, face to face or online 2. Completion of graded assignment on cross-plotting and quick scan estimation
GEOL 7323 Badge 3: Evaluating Shale Reservoirs
In this segment, students will focus on the evaluation of conventional and unconventional shale and shaly sand reservoirs. First, students gain an understanding of when global source rocks were formed by looking at depositional characteristics such as basin restriction, nutrient levels, sedimentation rates, and sea-level rise. Then, they apply their knowledge of physical property logs to evaluate shale and shaly sand formations. Topics also include a discussion of the pros and cons of various modeling techniques.
1. 15 hours of instruction, face to face or online 2. Completion of graded assignment
GEOL 7323 Petrophysics and Formation Evaluation Belt
This belt is focused on understanding and determining reservoir thickness and volume of hydrocarbons through the modeling and measurement of rock properties. The quality of these primary datasets is critical for data analytics and AI applications at the hydrocarbon reservoir, field, play, depositional basin, and global scales. There are several approaches that are used to evaluate this at each stage of development prior to, during, and after drilling. These include the use of cores and core analysis, mud logging, measurement/logging while drilling, wireline logging, memory logging, testing (i.e., wire length formation test, drill stem test, production testing). Badge 1: Modeling Rock Properties - Understanding what is meant by a model and how models are built is fundamental to evaluating rock formations for hydrocarbon potential. Models are highly dependent on the lithology and petrophysics of source rocks and reservoir rocks, including porosity and permeability. Topics covered in this badge include traditional reservoir rock and fluid properties and concludes with a review of saturation height modeling. Upon completion of this course, students will understand how and why modeling is important for evaluating formations and how to build a saturation height model based on rock properties. Badge 2: Introductory Logging - Well logging is a vital component in evaluating the hydrocarbon potential of rocks because it is the first physical evidence of the rocks penetrated by a well. Topics covered in this badge include measuring and interpreting properties such as porosity, resistivity, SP, gamma, and other reservoir indicators and how to compare and interpret properties using cross-plots. Quick scanning techniques and Rwa calculations are also discussed. Upon completion of this course, students will be able to interpret physical property logs of wells and evaluate their hydrocarbon potential. Badge 3: Evaluating Shale Reservoirs - In this segment, students will focus on the evaluation of conventional and unconventional shale and shaly sand reservoirs. First, students gain an understanding of when global source rocks were formed by looking at depositional characteristics such as basin restriction, nutrient levels, sedimentation rates, and sea-level rise. Then, they apply their knowledge of physical property logs to evaluate shale and shaly sand formations. Topics also include a discussion of the pros and cons of various modeling techniques.
45 hours of instruction, face to face or online. Completion of graded assignment on saturation height modeling. Completion of graded assignment on cross-plotting and quick scan estimation.
GEOL 6393: Seismic Amplitude Interpretation Gold Belt
A geophysical interpretation of a seismic anomaly consists of two general components. One relates to the anomaly’s amplitude and the other to the spatial shape of the anomaly. The interpretation of the seismic amplitude validates the composition of the reservoir while the interpretation of the anomaly’s spatial distribution validates the structural and stratigraphic framework. This belt focuses on both the theoretical and practical application of amplitude interpretation. Seismic amplitude interpretation, like other direct hydrocarbon indicators, is subject to machine learning and data analytics applications. Upon completion of this belt, participants will include well-log curves in seismic interpretation; recognize hydrocarbon signatures in different rock-property environments; model and interpret CDP Amplitude Variation with Offset (AVO); model seismic rock properties as a function of porosity, rock type, and pore fluid; and, conduct an AVO interpretation for reservoir SW, lithology, porosity and thickness. Badge 1: Rock Physics and Fluid Substitution - This segment covers concepts of well-log analysis and rock physics. Topics include the basics of well logging, well-log interpretation, velocity and elastic transforms, velocity-density models, grain and pore-fluid properties, and pore-fluid substitution with Gassmann’s equation. Exercises include interpretation of a typical well-log suite for model parameters needed to predict seismic AVO responses for both brine- and hydrocarbon saturation trends in the exploration area. Badge 2: Seismic Reflection Amplitude - Understanding and interpreting seismic amplitude is fundamental to geophysics and hydrocarbon exploration. This segment reviews: seismic amplitude and the subsequent pitfalls from 2D interpretation; seismic amplitude relationship to rock properties for various incident angles; and the relationship of amplitude to various bed geometries. Recognizing hydrocarbon signatures will also be covered, including classification, AVO field examples and inversion, and direct detection indicators (DDI). Participants will complete exercises on thin-bed modeling, attenuation, and AVO modeling of hydrocarbon- versus brine-saturated reservoirs. Badge 3: Interpretation Using Seismic Attributes - Understanding and interpreting seismic amplitude is fundamental to geophysics and hydrocarbon exploration. This segment reviews: seismic amplitude and the subsequent pitfalls from 2D interpretation; seismic amplitude relationship to rock properties for various incident angles; and the relationship of amplitude to various bed geometries. Recognizing hydrocarbon signatures will also be covered, including classification, AVO field examples and inversion, and direct detection indicators (DDI). Participants will complete exercises on thin-bed modeling, attenuation, and AVO modeling of hydrocarbon- versus brine-saturated reservoirs.
45 hours of instruction, face to face or online. Completion of exercises and daily quizzes: Exercise: Interpretation of typical well-log responses, Pore fluid substitution, Thin-bed modeling, Attenuation dispersion, AVO modeling salt vs. gas, AVO calibration with thin-bed modeling and Completion of two quizzes per badge.
GEOL 6393 Badge 1: Rock Physics and Fluid Substitution
This segment covers concepts of well-log analysis and rock physics. Topics include the basics of well logging, well-log interpretation, velocity and elastic transforms, velocity-density models, grain and pore-fluid properties, and pore-fluid substitution with Gassmann’s equation. Exercises include interpretation of a typical well-log suite for model parameters needed to predict seismic AVO responses for both brine- and hydrocarbon saturation trends in the exploration area.
1. 15 hours of instruction, face to face or online 2. Completion of exercises and daily quizzes a. Exercise: Interpretation of typical well-log responses b. Exercise: Pore fluid substitution c. Completion of two quizzes
GEOL 6393 Badge 2: Seismic Reflection Amplitude
Understanding and interpreting seismic amplitude is fundamental to geophysics and hydrocarbon exploration. This segment reviews: seismic amplitude and the subsequent pitfalls from 2D interpretation; seismic amplitude relationship to rock properties for various incident angles; and the relationship of amplitude to various bed geometries. Recognizing hydrocarbon signatures will also be covered, including classification, AVO field examples and inversion, and direct detection indicators (DDI). Participants will complete exercises on thin-bed modeling, attenuation, and AVO modeling of hydrocarbon- versus brine-saturated reservoirs.
1. 15 hours of instruction, face to face or online 2. Completion of exercises and daily exams a. Exercise: Thin-bed modeling b. Exercise: Attenuation dispersion c. Completion of two quizzes
GEOL 6393 Badge 3: Interpretation Using Seismic Attributes
1. 15 hours of instruction, face to face or online 2. Completion of exercises and daily exams a. Exercise: AVO modeling salt vs. gas b. Exercise: AVO calibration with thin-bed modeling c. Completion of two quizzes
In this course, we will learn how to use principles of seismic stratigraphy, seismic geomorphology, structural geology, and rock physics to interpret seismic reflection data and associated attributes to delineate faults, fractures, folds, fluvial-deltaic complexes, turbidites, mass transport complexes, karst, and other structural and stratigraphic features of interest. At the end of the course, the student will be able to identify major geologic features on 3D seismic data, use 3D visualization to effectively communicate these features to others, identify and interpret through seismic acquisition and processing artifacts, generate simple seismic time/structure maps, and generate and interpret simple attribute extraction maps. Along the way, the student will become adept at exploiting modern 3D data interpretation workstation software. The process of 3D visualization and interpretation is interactive with machine learning and data analytics. Badge 1: Basic 3D Seismic Interpretation and Evaluating Seismic Data Quality - This segment will cover some of the basics of seismic interpretation, including impedance, reflection coefficients, and the convolutional model. Students will also learn how to visualize 3D data in Petrel and how to interpret these visualizations for hydrocarbon indicators, complex trace, horizon, and formation attributes. Students will learn to identify seismic noise and imaging artifacts that can be ameliorated by better acquisition and processing as well as seismic data conditioning that can be performed on the interpreter’s desktop. Badge 2: Modern Seismic Attributes - Building on the basics of 3D seismic interpretation, this segment will shoewith more modern seismic attributes including structural dip, curvature, aberrancy, reflector convergence, coherence, amplitude gradients, texture, and spectral decomposition. of the basics such as geometric attributes that map continuity and texture, spectral decomposition, and thin-bed tuning. Then students will review the 3D seismic expression of tectonic deformation. Badge 3: Seismic Expression of Tectonic Deformation and the Environment of Deposition - Students will learn how to use 3D seismic data and modern seismic attributes to map structure, stratigraphy, and diagenetic alteration. Examples will show that through the use of an appropriate geologic model these maps along with well control can be used to predict areas that are more likely to be fractured, have more sand, or present possible drilling hazards.
45 hours of instruction, face-to-face or online. Completion of labs per badge.
GEOL 6390 Badge 1: Basic 3D Seismic Interpretation and Evaluating Seismic Data Quality
This segment will cover some of the basics of seismic interpretation, including impedance, reflection coefficients, and the convolutional model. Students will also learn how to visualize 3D data in Petrel and how to interpret these visualizations for hydrocarbon indicators, complex trace, horizon, and formation attributes. Students will learn to identify seismic noise and imaging artifacts that can be ameliorated by better acquisition and processing as well as seismic data conditioning that can be performed on the interpreter’s desktop.
1. 15 hours of instruction, face-to-face or online 2. Completion of labs a. Lab: Data loading, 3D visualization, and color bars b. Lab: Instantaneous attributes c. Lab: Interpreting faults d. Lab: Interpreting horizons in Petrel
GEOL 6390 Badge 2: Modern Seismic Attributes
Building on the basics of 3D seismic interpretation, this segment will shoewith more modern seismic attributes including structural dip, curvature, aberrancy, reflector convergence, coherence, amplitude gradients, texture, and spectral decomposition. of the basics such as geometric attributes that map continuity and texture, spectral decomposition, and thin-bed tuning. Then students will review the 3D seismic expression of tectonic deformation.
1. 15 hours of instruction, face-to-face or online 2. Completion of labs a. Lab: Generating surfaces in Petrel b. Lab: Horizon slices, phantom horizons, stratal slices, and isochrons c. Lab: Computing structural dip magnitude and dip azimuth of mapped surfaces
GEOL 6390 Badge 3: Seismic Expression of Tectonic Deformation and the Environment of Deposition
Students will learn how to use 3D seismic data and modern seismic attributes to map structure, stratigraphy, and diagenetic alteration. Examples will show that through the use of an appropriate geologic model these maps along with well control can be used to predict areas that are more likely to be fractured, have more sand, or present possible drilling hazards.
1. 15 hours of instruction, face-to-face or online 2. Completion of labs a. Lab: Computing volumetric dip, curvature, variance, and other modern attributes available in Petrel b. Lab: Computing spectral components c. Lab: Structure-oriented filtering
The principles of basin modeling and how the development of a sedimentary basin is controlled by the Earth’s structure, its structural deformation, and the interplay between uplift, subsidence, and sediment load in the full development of a basin through time. Further, how those processes impact and develop the formation of petroleum systems is discussed. Lectures include reviews of basin modeling, the petroleum system, the Earth’s interior, cratonic basins and the role of mantle processes, rheology of the lithosphere, strength of the lithosphere and strength profiles, and finally flexural basins. Also, access to and fundamentals of Petromod are discussed.
1. 15 hours of instruction, face-to-face or online. 2. Successful completion of the Foreland Basin Exercise. 3. Participation in the discussion of the foreland basin exercise.
This segment of the course includes a Petromod modeling exercise and discussion on the San Joaquin Basin. An introduction is given for basin modeling of rift basins and rift margins and the use of the McKenzie stretching model and the Wernicke extension model. The fundamentals of how heat flow relates to maturation rates and the use of backstripping to determine the geologic timing of the elements of a fully developed petroleum system. Finally, the Petromod modeling exercise of the Anadarko Basin will be reviewed and discussed.
1. 15 hours of instruction, face-to-face or online. 2. Completion of exercise on modeling the Anadarko Basin. 3. Participation in discussion of the Anadarko Basin model results.
GEOL 6351 Badge 3: Integration and the Energy Transition
Introductions are given for basin modeling in forearc basins, back-arc basins, and pull-apart basins. Integrating machine learning with basin modeling results is explained and discussed. The role of basin modeling for the development of geothermal energy and the energy transition is introduced. Feedback on the Anadarko Basin Petromod exercise is provided.
1. 15 hours of instruction, face-to-face or online 2. Successful completion of Basin Modeling exam.
This basin modeling class relies on basin modeling software that can be integrated with cognitive environments that include data analytics, machine learning, and artificial intelligence components. The modeling software allows static models and dynamic simulations of most petroleum systems as well as carbon storage workflows. The course provides insights and examples from multiple types of sedimentary basins. The basics to the formation of basins and their evolution through time are reviewed as each type of basin presents different processes and rates for the creation, maturation, migration, and accumulation of hydrocarbon resources. Badge 1: Basin Modeling Intro The principles of basin modeling and how the development of a sedimentary basin is controlled by the Earth’s structure, its structural deformation, and the interplay between uplift, subsidence, and sediment load in the full development of a basin through time. Further, how those processes impact and develop the formation of petroleum systems is discussed. Lectures include reviews of basin modeling, the petroleum system, the Earth’s interior, cratonic basins and the role of mantle processes, rheology of the lithosphere, strength of the lithosphere and strength profiles, and finally flexural basins. Badge 2: Basin Modeling This segment of the course includes a Petromod modeling exercise and discussion on the San Joaquin Basin. An introduction is given for basin modeling of rift basins and rift margins and the use of the McKenzie stretching model and the Wernicke extension model. The fundamentals of how heat flow relates to maturation rates and the use of backstripping to determine the geologic timing of the elements of a fully developed petroleum system. Finally, the Petromod modeling exercise of the Anadarko Basin will be reviewed and discussed. Badge 3 Integration and the Energy Transition: Introductions are given for basin modeling in forearc basins, back-arc basins, and pull-apart basins. Integrating machine learning with basin modeling results is explained and discussed. The role of basin modeling for the development of geothermal energy and the energy transition is introduced. Feedback on the Anadarko Basin Petromod exercise is provided.
1. 45 hours of instruction, face-to-face or online. 2. Completion of exercise per badge. 3. Successful completion of Basin Modeling exam.
GEOL 6379 Badge 1: Applied Biostratigraphy Intro
Overview and discussion of the different types of biostratigraphic data and quality based on detail, sources, and vintage. The nature of bioevents and the importance of large and diverse assemblages of data are explained and discussed. An introduction to the utility and benefits of each major micropaleontological group are presented. The basics of graphic correlation, composite standards, and paleoenvironmental interpretations are presented. A review of stratigraphy is also included.
1. 15 hours of instruction, face to face or online 2. Successful completion of test 1, a reading assignment on paleoenvironmental interpretation and an exercise on the basics of graphic correlation.
GEOL 6379 Badge 2: Graphic Correlation and Paleoenvironments
The processes of building composite standards with graphic correlation are explained and discussed in detail. Economic applications in real world exploration and development examples are presented to give the student an understanding of the processes and their impact on resource development around the world. The same datasets are useful for paleoenvironmental interpretations that allow the characterization of potential source and reservoir/aquifer formations.
1. Instruction on, face to face or online 2. Successful completion of test 2, an exercise on paleoenvironmental interpretation.
GEOL 6379 Badge 3: Geochronology Methods
This segment will cover some of theory and application of thermochronology. Systems covered will include Rb-Sr, Re-Os, U-Pb, (U+Th+Sm)/He, fission track, and 40Ar/39Ar. Case studies pertaining to tectonic, stratigraphic, and basin evolution studies will be used. The usefulness of absolute dates from geochronology in combination with robust data sets on relative age dates in strata from biostratigraphy and sequence stratigraphy as well as the Law of Superposition will be discussed. Complexities of sampling for minerals and the geologic processes that can bias results are discussed.
1. 15 hours of instruction, face to face or online 2. Successful completion of test 3.
GEOL 6379: Applied Biostratigraphy Belt
This belt considers two major data sources to develop geologic models for global and localized time scales to address the timing of significant events of Earth history and the deposition of resource bearing formations that contain energy and water resources. These linear time models are developed with a reiterative process known as graphic correlation with output in the form of composite standards. Composite standards are made one stratigraphic section at a time in a process that mimics machine learning. Data from each new stratigraphic section is correlated to the existing composite standard whereby new bioevents can be added and the range of existing bioevents can be evaluated to capture the knowledge provided by each new well or outcrop dataset. This allows high-resolution timing of localized bioevents aid in the identification of valuable energy reservoirs and groundwater aquifers at otherwise unattainable levels of detail. These data also allow for detail interpretations of paleoenvironments that increase the accuracy of facies analyses through geologic time. The first two badges, as discussed above, address analytic approaches used to interpret massive biostratigraphic datasets whereas the third badge addresses many geochronologic tools with less robust data sets but very significant tie points to calibrate an overall biogeochronologic time scale to absolute time. Both methods provide chronologic evidence of the thermal history of sedimentary deposits in economically significant basins. Badge 1: Applied Biostratigraphy Intro Overview and discussion of the different types of biostratigraphic data and quality based on detail, sources, and vintage. The nature of bioevents and the importance of large and diverse assemblages of data are explained and discussed. Introduction to the utility and benefits of each major micropaleontological group are presented. The basics of graphic correlation, composite standards, and paleoenvironmental interpretations are presented. A a review of stratigraphy is also included. Badge 2: Graphic Correlation and Paleoenvironments The processes of building composite standards with graphic correlation are explained and discussed in detail. Economic applications in real world exploration and development examples are presented to give the student an understanding of the processes and their impact on resource development around the world. The same datasets are useful for paleoenvironmental interpretations that allow the characterization of potential source and reservoir/aquifer formations. Badge 3: Geochronology Methods This segment will cover some of theory and application of thermochronology. Systems covered will include Rb-Sr, Re-Os, U-Pb, (U+Th+Sm)/He, fission track, and 40Ar/39Ar. Case studies pertaining to tectonic, stratigraphic, and basin evolution studies will be used. The usefulness of absolute dates from geochronology in combination with robust data sets on relative age dates in strata from biostratigraphy and sequence stratigraphy as well as the Law of Superposition will be discussed. Complexities of sampling for minerals and the geologic processes that can bias results are discussed.
1. 45 hours of instruction, face to face or online 2. Completion of exercise per badge. 3. Successful completion of test per badge.
In this segment, students will review the basic science behind seismic waves, including elasticity, stress, strain, and Hooke’s law. This segment includes basic mathematical ideas and ends with an in-depth review of wave equations and through discussion of body wave solutions.
1. 15 hours of instruction, face-to-face or online 2. Completion of a quiz
GEOL 7333 Badge 2: Seismic Waves at Interfaces
The focus of this segment is how waves interact with interfaces: surface waves, reflections, and refractions of seismic waves. Surface waves are the most prominent arrivals in the data, but need to be removed to reveal the reflections, which are normally of greatest interest. These topics complete all of the standard discussion of seismic waves and rays, using standard assumptions.
GEOL 7333 Badge 3: Advanced Seismic Wave Topics
In this final part, we address complications that can arise, including multiples, diffractions, curved and rugose interfaces, Fresnel zones, and resolution issues. Then we consider the effects on wave propagation caused by failures of the simple assumptions made previously. These include the effects of poro-elasticity, attenuation and apparent attenuation, and seismic anisotropy.
1. 15 hours of instruction, face-to-face or online 2. Completion of an exam
This course will cover the principles and methods underlying the application of seismology to petroleum exploration. To use artificial intelligence for modeling, machine learning or deep learning of seismic data, one must first understand the properties of seismic waves and their response to specific lithologies and fluids. This course underscores the responses of various properties of seismic energy that are used to image or infer the subsurface structure, composition and textural traits sedimentary strata at the basin, prospect, and reservoir levels. Badge 1: Seismic Wave Basics - In this segment, students will review the basic science behind seismic waves, including elasticity, stress, strain, and Hooke’s law. This segment includes basic mathematical ideas and ends with an in-depth review of wave equations and through discussion of body wave solutions. Badge 2: Seismic Waves at Interfaces - The focus of this segment is how waves interact with interfaces: surface waves, reflections, and refractions of seismic waves. Surface waves are the most prominent arrivals in the data, but need to be removed to reveal the reflections, which are normally of greatest interest. These topics complete all of the standard discussion of seismic waves and rays, using standard assumptions. Badge 3: Advanced Seismic Wave Topics - In this final part, we address complications that can arise, including multiples, diffractions, curved and rugose interfaces, Fresnel zones, and resolution issues. Then we consider the effects on wave propagation caused by failures of the simple assumptions made previously. These include the effects of poro-elasticity, attenuation and apparent attenuation, and seismic anisotropy.
1. 45 hours of instruction, face-to-face or online 2. Completion of a quiz per badge
GEOL 7341 Badge 1: Seismic Data Analysis Basics
In this opening badge, students will review the basics of geophysical data processing, including data acquisition, processing, and interpretation. Then, there will be a review of preliminary seismic data analysis, discrete spectral analysis, and seismic resolution and fidelity.
1. 15 hours of instruction, face-to-face or online 2. Completion of two quizzes and one exam
GEOL 7341 Badge 2: Data Processing Basics
This segment covers some data processing techniques, including data filtering, deconvolution, and seismic migration. A review of filtering types is given.
1. 15 hours of instruction, face-to-face or online 2. Completion of one quiz and one exam
GEOL 7341 Badge 3: Data Analysis and Modeling
Students will learn seismic velocity analysis, including velocity trends, refraction, semblance, migration, and tomography. Then, students will utilize data fitting, model inversion, and analysis of borehole seismic data.
1. 15 hours of instruction, face-to-face or online 2. Completion of two quizzes
GEOL 7341: Geophysical Data Processing Belt
This course will cover the principles and methods of processing geophysical data in discrete form, particularly seismic data, for exploration of petroleum resources. Topics to be covered include sampling theory, spectral analysis, resolution, filtering, deconvolution, seismic migration, velocity model building, data fitting, and inversion. These processing methods provide the data for additional analysis, interpretation and modeling for machine learning and evaluation with artificial intelligence. Badge 1: Seismic Data Analysis Basics In this opening badge, students will review the basics of geophysical data processing, including data acquisition, processing, and interpretation. Then, there will be a review of preliminary seismic data analysis, discrete spectral analysis, and seismic resolution and fidelity. Badge 2: Data Processing Basics - This segment covers some data processing techniques, including data filtering, deconvolution, and seismic migration. A review of filtering types is given. Badge 3: Data Analysis and Modeling - Students will learn seismic velocity analysis, including velocity trends, refraction, semblance, migration, and tomography. Then, students will utilize data fitting, model inversion, and analysis of borehole seismic data.
1. 45 hours of instruction, face-to-face or online 2. Completion of quizzes and exam per badge
This is an educational program on petroleum data analytics techniques and practices in the oil and gas industry. Learners will be able to implement data-driven methods on surface and subsurface data including but not limited to drilling, completions, and reservoir engineering. Learners will implement data-driven and data analytics workflow on real-world case studies and petroleum engineering problems. The hands-on nature of the course will allow the learners to integrate data science and machine learning methods into the application of petroleum engineering data science problems. Software supporting instruction include Python, Spotfire, Jupyter notebook, VS Code.
Introduction, getting started with python and important libraries Overview of data analytics and machine learning applications in petroleum engineering, Data cleaning, and wrangling, Exploratory data analysis and data visualization techniques Assessments based on the above topics are used and graded to provide evidence that learners have mastered the subject matter and its applications.
Silver Badge 2: Supervised and Unsupervised Learning
Review of statistics and analytics techniques, Regression and trees, Supervised learning, Unsupervised learning Assessments based on the above topics are used and graded to provide evidence that learners have mastered the subject matter and its applications.
Silver Badge 3: Business Problem Framing
Time series analysis, Text analytics, Business problem framing Assessments based on the above topics are used and graded to provide evidence that learners have mastered the subject matter and its applications.
Big Data Analytics for Petroleum Engineers Silver Belt
The Silver Belt is awarded to learners who successfully complete Badges 1, 2 & 3 of the program.
Fundamentals of rubber science, Structure, Glass transition temperature, Impacts of molecular structure, Gelation, Cross-linking chemistry, and Rubber elasticity. Rubber compounding, various rubbers, and ingredients, principles of compounding for extreme environment. Behavior of rubbers at very high temperatures, Aging of pristine rubber, Identification of molecular species by experiments and confirmation by simulation, Aging of Rubber vulcanizates, Thermo-mechanical properties, and Physical and Chemical Aging. Failure of rubber - Mechanical properties, Impact of aging on mechanical properties; correlating molecular changes to macroscopic properties. Behavior of rubbers in high pressure and high temperature, Gaseous and Liquid Environments, Thermodynamics and Kinetics. Rubber durability in liquid and corrosive environments. Practical failure of rubber products in an aggressive environment and their causes; Rapid Gas Decompression; Extrusion failure, Repetitive Cyclical and Dynamic Stresses; Understanding metal–rubber interface interactions Rubber life estimation, End of life analysis and monitoring, Monitoring protocols
The participants were evaluated by the following criteria: Attendance Participation in the form of group discussion Answering the quizzes online for each of the eight sessions Passing the above with a grade above 60%.
Data Processing and Machine Learning Badge 1
Badge holders explored one of the four data analytics modules called exploratory data analysis, a necessary step to clean and format data before building machine learning models. Supervised machine learning concepts were introduced, and participants built data-driven models via supervised machine learning algorithms. Two machine learning algorithms - kNN and Decision Tree Classifier were covered in this badge. 12 PDH/1.2 CEU
• Complete 12 hours of online instruction • Complete homework exercises • Achieve a passing grade of at least 80% in qualifying tests Exploratory Data Analysis and Data Preprocessing • Introduction to Data Analytics • Exploratory Data Analysis (EDA) (Visualization, and Descriptive Statistics) • Data Preprocessing (including Principal Component Analysis) Supervised Machine Learning •kNN • Decision Tree Classifier Data Analytics, Descriptive Statistics, Data Processing, Data Visualization, Supervised Machine Learning, Classification, kNN, Decision Tree
No Code Analytics and Machine Learning in Energy - Badge 2 Model Evaluation and Clustering
Participants used Model evaluation matrices (e.g., confusion matrix, ROC, AUC, etc.) and model evaluation methods (e.g., 10-fold cross validation) and improved model performance through automatic tuning of model parameters. Participants completed a hands-on session on Model Evaluation and were introduced to more advanced machine learning techniques of ensemble methods. Participants learned to implement the complete data mining pipeline including model building and model evaluation in Orange. Participants were introduced to unsupervised machine learning algorithms for cluster analysis. 12 PDH/1.2 CEU
• Complete 12 hours of online instruction • Complete homework exercises • Achieve a passing grade of at least 80% in qualifying tests Evaluating and Improving Model Performance • Evaluating model performance • Improving model performance Ensemble Methods • Ensemble Methods (Bagging, Boosting and Random Forest) Unsupervised Machine Learning Model performance, Bagging, Boosting, Random Forest, Unsupervised Machine Learning, Cluster Analysis, k-Means
Alternate Machine Learning Algorithms Badge 3
Participants used 5 other popular supervised machine learning algorithms and learned concepts behind these algorithms and completed hands-on labs using data set. Participants learned how these algorithms are used for tackling classification and regression problems through hands-on labs. Participants also used Self Organizing Maps, a type of ANN that uses unsupervised machine learning for dimensionality reduction. 12 PDH/1.2 CEU
• Complete 12 hours of online instruction • Complete homework exercises • Achieve a passing grade of at least 80% in qualifying tests Other popular Machine Learning algorithms • Linear Regression Logistic Regression • Naïve Bayes (Probabilistic Learning) • Support Vector Machines (SVM) • Artificial Neural Network (ANN) Self Organizing Maps (SOM) Supervised Machine learning, Classification, Regression Analysis, Naïve Bayes, Probabilistic Learning, Support Vector Machines, Artificial Neural Network, Self Organizing Maps,
Awarded for successful completion of all 3 Bronze badges
Complete 36 hours of online instruction • Complete homework exercises • Achieve a passing grade of at least 80% in qualifying tests Data Analytics, Descriptive Statistics, Data Processing, Data Visualization, Supervised Machine Learning, Classification, kNN, Decision Tree Model performance, Bagging, Boosting, Random Forest, Unsupervised Machine Learning, Cluster alysis, k-Means Supervised Machine learning, Classification, Regression Analysis, Naïve Bayes, Probabilistic Learning, Support Vector Machines, Artificial Neural Network, Self Organizing Maps,
No Code Analytics and Machine Learning in Energy
• Complete 12 hours of online instruction • Complete homework exercises • Achieve a passing grade of at least 80% in qualifying tests Other popular Machine Learning algorithms • Linear Regression Logistic Regression • Naïve Bayes (Probabilistic Learning) • Support Vector Machines (SVM) • Artificial Neural Network (ANN) Self Organizing Maps (SOM)
Badge holders comprehensively understood the robot integration process, including economic evaluation and qualification. They also learn the evaluation tool/protocol for risk and safety management, enabling participants to assess the feasibility of incorporating robots into their working environment.
• Complete 14 hours of in-person instruction • Complete in-class tests • Achieve a passing grade of at least 80% in qualifying tests • Understand the critical safety, risk, and economic issues in adopting robots. • Gain technology awareness in remote operation, system integrity, and AI, focusing on robotics. • Robotics • Safety & reliability • Remote operation • Digital transformation • Underwater robotics
Badge holders comprehensively understood CCUS as a fundamental requirement for successful de-carbonization, and its significance recognized by the International Energy Agency and the US Department of Energy. They also successfully identified the global market framework, carbon capture sources and systems, transportation, storage and utilization, legal issues and policy related to CCUS, implementation, and how to execute a full-scope synthesis of a CCUS project.
• Complete 9 online modules around CCUS. • Complete all 7 quizzes, course discussion, and pre- and post-evaluation. • Achieve a passing grade of at least 80% in qualifying assessments. • Understand that CCUS is a fundamental requirement for successful de-carbonization. • Successfully identify the global market framework, carbon capture sources and systems, transportation, storage and utilization, legal issues and policy related to CCUS, implementation, and how to execute a full-scope synthesis of a CCUS project. • Carbon capture, utilization, and storage (CCUS) • Energy transition • Sustainability • Executive education
Elastomers: Behavior & Failure in Critical Environments
Badge holders comprehensively understood the fundamentals of elastomer science, various elastomers and their principles of compounding, behavior, and failure of elastomers in tire, non-tire, and oil and gas industry products, aging of elastomers and their products, behavior of elastomers in critical environments, elastomer testing, practical failure of elastomer products, end-of-life analysis, monitoring, life-time prediction, etc.
• Attend four 4-hour online live sessions around Elastomers. • Complete all 10 quizzes, and pre- and post-evaluation. • Achieve a passing grade of at least 70% in qualifying assessments. • Successfully identify knowledge and principles around: o Elastomers and elastomeric compounds that are used in critical applications; o Fundamentals of elastomer science to make materials better with long life; o Behavior of elastomers and soft materials under ambient and extreme conditions. o Analysis of elastomers for prevention of failure. o Failure of elastomers in practical situations. o Life prediction and monitoring. • Elastomers in Critical Environments. • Energy applications. • Sustainability.
AI & Robotics for Energy Integrity Spring 2024
Badge holders comprehensively understood AI’s role in analyzing and interpreting data derived from robots, and became familiar with the model training process. Badge holders had a solid grounding in deep learning and computer vision; understood how robots perceive, analyze, and act in their surroundings; were able to apply the skills to use AI in complex data analysis and management; and developed expertise in applying AI & Robotics to address needs, challenges, and opportunities within the energy sector.
• Complete 7 online modules around AI & Robotics. • Complete all quizzes, pre- and post-evaluation. • Achieve a passing grade of at least 70% in qualifying assessments. • Understand how AI could be applied in analyzing and interpreting data derived from robotics. • Successfully identify connections between AI, Robotics, deep learning, and computer vision. • Have knowledge to apply the skills to use AI in complex data analysis and management; and developed expertise in applying AI & Robotics to address needs, challenges, and opportunities within the energy sector.
Automation and Autonomy of Mobile Robots Using ROS
Badge holders comprehensively understood the electrical and programming fundamentals of robotics, and some technical tools commonly used by industry and academia. Badge holders had a solid grounding in (1) electrical and embedded system basics; (2) using the Robotics Operating System (ROS) and applying Simultaneous Localization and Mapping (SLAM); (3) putting together robots and completing programming.
• robotics • automation & autonomy • ros • slam •
• Complete three in-person lectures and corresponding modules around Automation and Autonomy. • Complete the lab session by assembling the car and completing programming. • Complete all quizzes, pre- and post-evaluation. • Achieve a passing grade of at least 80% in qualifying assessments.
Badge holders comprehensively identify key concepts and practices in plastic recycling. They demonstrate an understanding of current recycling processes, the differences between circular and linear economies, and how materials recovery facilities manage waste. Participants also explore the environmental impacts of plastic waste on water and soil, examine current applications of recycled plastics, and assess how policy and regulations influence recycling effectiveness. They gain foundational knowledge of life cycle and technoeconomic analyses as applied to plastics and recycling, with a well-rounded perspective on sustainable materials management.
• Complete 11 online modules around Plastics Circular Economy. • Complete all 3 assessments, live course discussion, and pre- and post-evaluation. • Achieve a passing grade of at least 80% in qualifying assessments. • Successfully achieved all learning objectives.
Advances of Elastomers: Science & Technology
Badge holders comprehensively understood fundamentals of elastomer science, advances of different elastomers and their composites, sustainability issues, challenges of product manufacturing, and applications.
•advances of elastomers •energy applications
• Attend four 4-hour online live sessions around Elastomers. • Complete all quizzes, pre- and post-evaluation. • Achieve a passing grade of at least 70% in qualifying assessments. • Successfully identify knowledge and principles around: o Advances in elastomers and elastomeric compounds o Natural monomers o Fundamentals of elastomer science to make materials superior with long life o Sustainable materials o Recycling technology o Behavior of elastomers o Advances in tire technology o Advances of elastomers and their compounds in oil and gas industries o Environmental analysis/Net zero carbon economy
Electrification in Energy Bronze Badge 1
Electrification in Energy Bronze Badge 1 badge image
Badge holders comprehensively understood foundational concepts necessary for a better understanding of electrical systems in energy, offshore and subsea. Topics include 3-phase circuits, electric power systems, power electrics, and electric machines.
•electrical engineering •electrification in energy
• Attend all the six 3-hour online live sessions around Electrification in Energy. • Attend the optional in-person lab session. • Complete all quizzes, pre- and post-evaluation. • Achieve a passing grade of at least 80% in qualifying assessments. • Successfully identify fundamental knowledge and principles around electrical systems in energy, offshore and subsea.