Disciplines
Back
101
0
In the rapidly evolving technological landscape, data science and machine learning have emerged as transformative forces across a multitude of industries. The oil and gas industry, traditionally slower to adopt such advancements, is now actively integrating these technologies to enhance operational efficiency and exploration efforts. In response to this growing demand, we have designed a comprehensive training course tailored specifically for professionals in the oil and gas industry who seek to harness the power of data analysis and machine learning in their projects and operations.
The course begins with an introduction to Python, one of the most widely-used programming languages in data science. It emphasizes its industry-specific applications, teaching participants to write efficient Python programs and use its libraries such as NumPy, Pandas, Matplotlib, and SciPy for numerical operations, data input/output, scientific plotting, and interpolation respectively.
The second module dives deeper into data analysis and visualization, honing in on techniques essential for the oil and gas industry. Participants will become proficient in managing data cleansing, creating various visualizations, handling missing data, and interactive plotting using tools such as Plotly.
The third module focuses on production data analysis and forecasting. Here, participants will learn to conduct time-series analysis, visualizing production data, identifying and eliminating outliers, and performing decline curve and uncertainty analyses. These skills are vital in maximizing the efficiency and predictability of production operations in the oil and gas sector.
The course then proceeds to explore the applications of machine learning, both for lithofacies classification and well log synthesis. Participants will be introduced to the workflow of creating and evaluating classification and regression models, enabling them to classify lithofacies and predict well logs effectively.
Finally, the eighth module provides a comprehensive introduction to machine learning fundamentals, touching upon critical concepts such as supervised and unsupervised learning, dimensionality reduction, and explainable machine learning. Practical workshops are woven into this module, allowing participants to put their knowledge into practice by reconstructing wireline logs using random forests and performing outlier detection and clustering.
Upon completion, participants will not only have a solid foundation in Python programming and data analysis but will also understand how to implement machine learning models in their oil and gas operations and projects. This course, thus, provides an excellent opportunity for industry professionals such as petrophysicists, geologists, engineers, IT personnel, and project managers, to upgrade their skills and knowledge in line with industry trends, enhancing both their individual capabilities and their organization's competitive edge.
- Petrophysicists: Professionals who specialize in the study of physical and chemical rock properties and their interactions with fluids.
- Geologists: Experts involved in studying the solid and liquid matter that constitutes the Earth and other terrestrial planets.
- Production Engineers: Specialists who oversee and optimize the production of hydrocarbons in oil and gas operations.
- Reservoir Engineers: Engineers responsible for optimizing the production of oil and gas via proper well placement, production rates, and enhanced oil recovery techniques.
- IT Personnel: Those who manage and ensure the efficient use of IT resources within the organization.
- Petroleum Engineers: Experts who design and develop methods for extracting oil and gas from deposits below the Earth's surface.
- Project Engineers and Managers: Professionals who oversee oil and gas projects, ensuring they are completed safely, within budget, and on time.
- Computer Programmers: Individuals responsible for writing, testing, and maintaining the code that makes up computer software.
- Additionally, this course is also apt for any individuals who are interested in learning Python programming, data science, machine learning, and their potential applications in the oil and gas industry.
- Write Effective Python Programs: Leverage Python's capabilities for numerical operations, data handling, and scientific plotting, specifically tailored for the oil and gas industry
- Analyze and Visualize Data: Execute advanced techniques for oil and gas data analysis and visualization, including managing missing data issues and creating interactive plots
- Forecast Production Data: Perform time-series analysis of production data, visualizing it effectively, identifying and eliminating outliers, and undertaking decline curve and uncertainty analyses
- Apply Machine Learning for Classification: Develop lithofacies classification models using machine learning, understand how to read and visualize well log data and lithofacies, utilize classifiers in Scikit-Learn, and evaluate classifier performance with cross-validation
- Apply Machine Learning for Regression: Use regression for well log synthesis, learn about regressors in Scikit-Learn, and evaluate regressor performance with cross-validation
- Understand Machine Learning Fundamentals: Grasp the basic concepts and types of machine learning, use Python's scikit-learn for machine learning tasks, apply supervised learning with regression, and perform hands-on tasks such as reconstructing wireline logs using random forests
- Use Unsupervised Learning Techniques: Learn the basics of unsupervised learning for dimensionality reduction, clustering, and outlier detection, with a focus on practical applications like outlier detection and clustering of wireline logs
- Interpret Machine Learning Models: Understand the basics of explainable machine learning to interpret predictions made by machine learning models and ascertain the importance of different variables
- This knowledge and skillset will empower participants to use Python, data science techniques, and machine learning effectively in their roles within the oil and gas industry, enhancing both operational efficiency and strategic decision-making capabilities
- Fundamentals of Python Programming
- Leveraging NumPy for Numerical Operations
- Optimizing Loop Processes with List Comprehension
- Creating Scientific Plots with Matplotlib
- Mastering Data Input and Output with NumPy and Pandas
- An Introduction to Interpolation with SciPy
- Techniques for Data Loading, Cleansing, and Preparation
- Using Pandas for Advanced Data Analytics
- Creating Data Visualizations (Bar Graphs, Pie Charts, Box Plots, KDE Plots)
- Interactive Plotting with Plotly
- Handling Missing Data Issues in Oil and Gas Industry
- Time-Series Analysis of Production Data
- Visualization Techniques for Production Data
- Outlier Detection and Elimination in Production Data
- A Comprehensive Guide to Decline Curve Analysis
- Uncertainty Analysis in Decline Curve Analysis
- Introduction to Classification Modeling Workflows
- Reading and Visualizing Well Log Data and Lithofacies
- A Guide to Classifiers in Scikit-Learn
- Evaluating Classifier Performance with Cross-Validation
- Introduction to Regression Modeling Workflows
- Reading Well Log Data and Visualizing Lithofacies
- An Overview of Regressors in Scikit-Learn
- Evaluating Regressor Performance with Cross-Validation
- Introduction to Machine Learning: Foundations and Types
- Python Scikit-learn: Unveiling Pipelines and Workflows
- Supervised Learning with Regression: From Linear to Random Forest Regression
- Hands-On Workshop: Reconstructing Wireline Logs Using Random Forests
- Unsupervised Learning: Dimensionality Reduction, Clustering, and Outlier Detection
- Hands-On Workshop: Outlier Detection and Clustering of Wireline Logs
- Explainable Machine Learning: Deciphering the 'Black Box' of Machine Learning Models
Training can take place in 4 formats:
- Self-paced
- Blended learning
- Instructor-led online (webinar)
- Instructor-led offline (classroom)
Description of training formats:
- Self-paced learning or e-Learning means you can learn in your own time and control the amount of material to consume. There is no need to complete the assignments and take the courses at the same time as other learners.
- Blended learning or "hybrid learning" means you can combine Self-paced learning or e-Learning with traditional instructor-led classroom or webinar activities. This approach requires physical presence of both teacher and student in physical or virtual (webinars) classrooms or workshops. Webinar is a seminar or presentation that takes place on the internet, allowing participants in different locations to see and hear the presenter, ask questions, and sometimes answer polls.
- Instructor-led training, or ILT, means that the learning can be delivered in a lecture or classroom format, as an interactive workshop, as a demonstration under the supervision and control of qualified trainer or instructor with the opportunity for learners to practice, or even virtually, using video-conferencing tools.
When forming groups of students, special attention is paid to important criteria - the same level of knowledge and interests among all students of the course, in order to maintain stable group dynamics during training.
Group dynamics is the development of a group in time, which is caused by the interaction of participants with each other and external influence on the group. In other words, these are the stages that the training group goes through in the process of communicating with the coach and among themselves.
The optimal group size for different types of training:
- Self-paced / E-learning: 1
- Instructor-led off-line (classroom): 6 – 12
- Instructor-led on-line (webinar): 6 – 12
- Blended learning: 6 – 12
- Workshop: 6 – 12
- On-the-job: 2 – 4
- Simulator: 1 – 2
Feedback in the form of assessments and recommendations is given to students during the course of training with the participation of an instructor and is saved in the course card and student profile.
In order to control the quality of the services provided, students can evaluate the quality and training programme. Forms of assessment of the quality of training differ for courses with the participation of an instructor and those that are held in a self-paced format.
For courses with an instructor, start and end dates are indicated. At the same time, it is important to pay attention to the deadlines for passing tests, exams and practical tasks. If the specified deadlines are missed, the student may not be allowed to complete the entire course programme.
A personal account is a space for storing your training preferences, test and exam results, grades on completed training, as well as your individual plan for professional and personal development.
Users of the personal account have access to articles and blogs in specialized areas, as well as the ability to rate the completed training and leave comments under the articles and blogs of our instructors and technical authors
Registered users of a personal account can have various roles, including the role of a student, instructor or content developer. However, for all roles, except for the student role, you will need to go through an additional verification procedure to confirm your qualifications.
Based on the results of training, students are issued a certificate of training. All training certificates fall into three main categories:
- Certificate of Attendance - students who successfully completed the course but did not pass the tests and exams can apply for a certificate of attendance.
- Certificate of Completion - students who have successfully completed a course could apply for a Certificate of Completion, this type of certificate is often required for compliance training.
- Verified Certificate - it is a verified certificate that is issued when students have passed exams under the supervision of a dedicated proctor.
You can always download a copy of your training certificate in PDF format in your personal account.
You will still have access to the course after completing it, provided that your account is active and not compromised and Tecedu is still licensed for the course. So if you want to review specific content in the course after completing it, or do it all over again, you can easily do so. In rare cases, instructors may remove their courses from the Tecedu marketplace, or we may need to remove a course from the platform for legal reasons.
During the training, you may encounter various forms of testing and knowledge testing. The most common assessment methods are:
- preliminary (base-line assessment) - to determine the current level of knowledge and adapt the personal curriculum
- intermediate - to check the progress of learning
- final - to complete training and final assessment of knowledge and skills, can be in the form of a project, testing or practical exam
Travel to the place of full-time training is not included in the cost of training. Accommodation during full-time studies can be included in the full board tuition fees.
While Tecedu is not an accredited institution, we offer skills-based courses taught by real experts in their field, and every approved, paid course features a certificate of completion or attendance to document your accomplishment.
You can preview samples of the training materials and review key information about the course on our website. You can also review feedback and recommendations from students who already completed this course.
We want you to be happy, so almost all purchased courses can be returned within 30 days. If you are not satisfied with the course, you can request a refund, provided the request complies with our return policy.
The 30-day money back policy allows students to receive quality teaching services with minimal risk, we must also protect our teachers from fraud and provide them with a reasonable payment schedule. Payments are sent to instructors after 30 days, so we will not process refund requests received after the refund period.
We reserve the right, in our sole discretion, to limit or deny refund requests in cases where we believe there is refund abuse, including but not limited to the following:
- A significant portion of the course has been consumed or downloaded by a student before the refund was requested.
- Multiple refunds have been requested by a student for the same course.
- Excessive refunds have been requested by a student.
- Users whose account is blocked or access to courses is disabled due to violation of our Terms and Conditions or the Rules of Trust and Security.
- We do not grant refunds for any subscription services.
- These refund restrictions will be enforced to the extent permitted by applicable law.
We accept most international credit and debit cards like Visa, MasterCard, American Express, JCB and Discover. Bank Transfers also may be an option.
Conducting classes is based on the fact that the teacher demonstrates text, drawings, graphics, presentations on an interactive board, while the content appears in the student's electronic notebook. A specially designed digital notepad and pen are used to create and edit text and images that can be redirected to any surface via a projector.
Classes are live streamed online, automatically recorded and published on the Learning Portal, allowing you to save them for reuse anytime, anywhere, on any mobile device. This makes it possible not to miss classes and keep up with classes and keep up with the passage of new material.
Real-life training uses the principles of game organization, which allows future professionals to rehearse and hone their skills in a virtual emergency. Learning as a game provides an opportunity to establish a connection between the learning activity and real life.
The technology provides the following learning opportunities:
- Focused on the needs of the user
- Instant feedback
- Independent decision making and choice of actions
- Better assimilation and memorization of the material
- Adaptive pace of learning tailored to the individual needs of the student
- Better transfer of skills learned in a learning situation to real conditions
Basic principles of training:
- A gradual increase in the level of difficulty in the game;
- Using a simplified version of a problem situation;
- Action in a variable gaming environment;
- The right choice is made through experimentation.
The main advantages of Game Based Learning technology:
- Low degree of physical risk and liability
- Motivation to learn while receiving positive emotions from the process;
- Practice - mirroring the real situation
- Timely feedback
- Choice of different playing roles
- Learning in collaboration
- Developing your own behavior strategy
Conducting practical classes online using remote access technologies for presentations, multimedia solutions and virtual reality:
- Laboratory workshops that simulate the operation of expensive bench equipment in real production
- Virtual experiment, which is visually indistinguishable from a remote real experiment performed
- Virtual instruments, which are an exact copy of real instruments
- Mathematical modeling to clarify the physical characteristics, chemical content of the investigated object or phenomenon.
Anton Gakhov
| Date | Duration | Location | Language | Format | Price |
|---|
Tecedu Home
Virtual Workshops
Industrial visualization
