| Code | Date | Time | Duration | Location | Currency | Team of 10 Per Person | Team of 7 Per Person | Early Bird Fee Per Person | Normal Fee Per Person |
|---|---|---|---|---|---|---|---|---|---|
| COURSE CODE : PEA / DAPF / 24 | 08 Dec 2024 | 8 PM Indian Time |
3 Hours Per Day
|
Zoom Online
|
Boost your team's skills and your budget! Enjoy group discounts for collaborative learning. Send an inquiry to info@peassociations.com.
Data Analytics Workflows for Artificial Lift, Production and Facility Engineers
This course is designed to equip Artificial Lift, Production, and Facility Engineers with data analytics tools and workflows. Participants will learn how to apply data-driven approaches to improve production performance, optimize artificial lift systems, and streamline facility operations.
Description
The "Data Analytics Workflows for Artificial Lift, Production, and Facility Engineers" course provides a comprehensive framework for leveraging data analytics to enhance operational efficiency in oil and gas production. You will gain practical skills in data-driven decision-making, performance optimization, and predictive maintenance using modern tools and workflows tailored for artificial lift systems and facility management.
This hands-on training emphasizes real-world applications of data analytics, allowing engineers to maximize output while minimizing downtime and operational costs. By the end of the course, participants will have a solid foundation in implementing data-driven strategies to achieve peak production performance.
Demo Class
In today's energy sector, data is a key asset in optimizing production and enhancing operational efficiency. This course introduces engineers to the latest data analytics workflows designed specifically for artificial lift systems and production facilities. Whether you are dealing with ESPs, gas lift, or rod pumps, this course will guide you through the process of using data to optimize your systems and improve decision-making.
This course will be delivered through a combination of interactive lectures, case studies, hands-on exercises, and practical examples. Participants will engage with real-world data sets and use industry-standard tools to practice the application of data analytics techniques.
This course is ideal for:
- Artificial Lift Engineers
- Production Engineers
- Facility Engineers
- Petroleum Engineers
- Operations Managers
- Maintenance Engineers
Digital Oil Field Data Explorations/Workflows
- Digital Transformation and Oilfields
- Key technologies for digital oilfields
- Oilfield System Data Verification and Management
A Brief/Incomplete Primer on ML/AI
- Data Science versus Data Analytics
- AI, ML and Deep Learning
- Data Analytics Lifecycle
- Bias-Variance-Complexity Tradeoff
- Data Preparation
- Model Types
- Role of Domain Knowledge
- Training & Evaluating Model
- Toolsets
System Setup & Checks
- Google CoLab – Why do we need it?
- Pull datasets & codebase from the GitHub repository
Data Workflows & Best Practices in Data Exploratory Analysis
- Data types in Production Domain: Streaming (Real-time or time-series) vs. Static (non-streaming)
- Data Processing Challenges
- Data Basics: Cleaning, filtration, and regulation
- Best practices on data exploratory analysis
Choke Flow Rate Study
Brief description of the data set/problem use case and expected outcome
- Problem, input & output variables
- Hands-On Exercise: Multiple ML Models & comparison
Rod Pump Dynamometer Card Classification
Brief description of the data set/problem use case and expected outcome
- Problem, input, and output variables definition – SPE paper
- Data set
- Hands-On Exercise: Model development & testing
Multiphase Flow Meter
Brief description of the data set/problem use case and expected outcome
- Problem, input & output variables – SPE Paper
- Hands-On Exercise: Multiple ML Models & comparison
On successful completion of this training course, PEA Certificate will be awarded to the delegates
