

Date | Time | Duration | Location |
---|---|---|---|
25-Nov 2024 | 9 PM Indian Time |
2 Hours Per Day
|
Zoom Online
|
The Classes Will be Online Via Zoom from Monday to Friday.
Applied Machine Learning for Production Engineering
This course equips production engineers with practical machine learning skills to analyze data and improve production efficiency. Participants will learn to apply data-driven decision-making in their operations, leading to enhanced productivity and reduced costs.
Description
In today’s fast-paced oil and gas industry, leveraging data is crucial for optimizing production processes. Our "Applied Machine Learning for Production Engineering" course provides participants with a comprehensive understanding of how to implement machine learning techniques to solve real-world production challenges. Through hands-on training and case studies, engineers will gain the skills needed to transform data into actionable insights, ultimately enhancing operational performance and decision-making.
Demo Class
Introduction
As the oil and gas industry evolves, the integration of advanced technologies such as machine learning is becoming increasingly essential. This course is designed for production engineers who seek to harness the potential of data analytics to improve production outcomes. By exploring key machine learning concepts and methodologies, participants will be well-equipped to tackle the complexities of modern production engineering.
Objectives
Training Methodology
Our training approach combines theoretical knowledge with practical applications. The course includes:
- Interactive lectures and discussions
- Hands-on workshops with real datasets
- Case studies from the oil and gas sector
- Group projects to foster collaboration and knowledge sharing
- Continuous assessment to track progress
Organisational Impact
By equipping production engineers with machine learning skills, organizations can significantly enhance their operational efficiency. Improved data analysis leads to better decision-making, reduced downtime, and optimized production processes. This course fosters a culture of innovation and data-driven thinking within the organization, enabling teams to respond effectively to industry challenges.
Personal Impact
Participants will gain valuable skills that are highly sought after in the oil and gas industry. This course enhances career prospects by providing expertise in machine learning, a key driver of innovation. Engineers will leave with practical knowledge they can immediately apply in their roles, contributing to personal and professional growth.
Who Should Attend?
This course is ideal for:
- Production Engineers
- Data Analysts
- Operations Managers
- Engineering Professionals seeking to upskill
- Anyone interested in the intersection of machine learning and production engineering
Day 1
- A gentle introduction to Python Programming Language
- Data Types and Structures in Python
- Introduction to Data Visualization
- Working with Tabulated Data using Pandas
- Basics of Data Cleaning and Transformation using Pandas.
- Creating Calculations and Data Exports.
- Linking Excel, CSV, TXT to Python
Day 2
- Introduction to the Concept of Clustering
- Understanding Distance Metrics
- Introduction to the Scikit-Learn Library for Machine Learning
- Anomaly Detection Techniques
Day 3
- Introduction to the Concept of Classification
- Voting and Decision Trees
- Introduction to kNN method
- Introduction to the Decision Tree and RandomForest Methods
- Python Plotting Techniques
Day 4
- Introduction to Continuous Data and Corresponding Relationships
- Relationship Visualization and Correlation Matrix
- Introduction to Regression Analysis
- Linear Regression Fundamentals
- Support Vector Regression (SVR)
- Xtreme Gradient Regression (XGBoost Library)
Day 5
- Introduction to Time-Bounded Data in the Oil and Gas Industry
- Understanding Typical Decline Curve Analysis (DCA) and Its Limitations
- Introduction to Time Series Analysis (TSA)
- Short-Term Production Prediction Using Time Series Analysis
- Simple Moving Average (SMA) and Exponential Moving Average (EMA)
-
Introduction to AutoRegressive (AR) Models
On successful completion of this training course, PEA Certificate will be awarded to the delegates
Mr. Nashat J. Omar With over 11 years of specialized experience in petroleum engineering, focus on production and flow assurance brings valuable expertise to the energy sector.
He possess a strong command of Python and C#, which empowers him to create efficient data management solutions and streamline workflows.
His collaborative nature and adaptability enable him to thrive in multidisciplinary settings, where he consistently contributes to success through innovative problem-solving.
He is dedicated to continuous learning and staying ahead of industry advancements, ensuring that he can enhance operational efficiency and guarantee robust flow assurance.