Data Analytics Workflows for Artificial Lift, Production and Facility Engineers (DAT 609)
COURSE SCHEDULE
| Code | Date | Location | price (€)* |
|---|---|---|---|
| DAT 609 | 12 - 13 Nov 2026 | Online | 1320 |
| DAT 609 | 10 - 11 Dec 2026 | Stavanger | 1760 |
COURSE OVERVIEW
Data analysis is the process of cleaning, inspecting, transforming, and modeling data to extract meaningful insights and support informed decision-making. In this hands-on course, participants will learn data analytics and data science techniques and workflows applied specifically to petroleum production, with particular emphasis on artificial lift systems. Through guided coding sessions and practical exercises, attendees will review and implement analytical workflows in realistic production scenarios.
The main objective of the course is to provide a clear understanding of data analytics and machine learning principles through practical application. While developing data-driven models, strong emphasis is placed on maintaining alignment with fundamental oil and gas production principles, ensuring that analytics results remain technically sound and physically meaningful.
COURSE OUTLINE
2 days
Day 1
o Digital Oil field and Applications in artificial lift
o What/Why/How of Digital Oil field / Digital Transformation
o Enablers of DoF in Artificial Lift
o If AL teams generate so much data, why are we not extracting the most value?
o Managerial challenges and change management
o Basics of Data Analytics
o What is ML/ AI?
o Popular Machine Learning Algorithms
o Regression vs. Classification vs. Clustering in Supervised & Unsupervised approaches
o What to do after the coding is done? Deployment challenges
o Data workflows & Best Practices in Data Exploratory analysis
o Available data: Streaming (Real-time or time-series) vs. Static (non-streaming)
o The required data frequency
o Data cleaning practices
o Best practices on data exploratory analysis
o Rod Pump Dynamometer Card Classification
o The problem, input, and output variables definition
o Neural Network Development
o Data set
Day 2
o Flow Pattern Prediction
o Problem, input, and output variables definition
o Data set
o Different ML / AI solutions
o Gas Lift Slugging
o Problem, input & output variables
o Regression Solution
o Virtual Flow Meter
o The problem, dataset – input/outputs
o Two- or three ML solutions
INSTRUCTOR
Dr. Rajan Chokshi
Dr. Rajan Chokshi works as an Artificial Lift and Production Optimization specialist with Accutant Solutions, a consulting firm based in Houston, USA. He brings over 34 years of experience across a national oil company, research consortia, consulting and software firms, and a service company, serving in roles including engineer, software developer, project manager, trainer, consultant, and senior business leader.
He has worked globally on projects involving multiphase flow, artificial lift, and production optimization, and regularly delivers webinars, workshops, and professional training programs through SPE and private forums.
As an Adjunct Faculty member, he has taught at Texas Tech University, Missouri University of Science & Technology, the University of Southern California, and currently at the University of Houston. He has served on several SPE committees, including Production & Facilities Advisory, Global Training, and Production Awards, and is the incoming Chair of the Awards & Recognition Committee. He has also co-chaired an SPE Artificial Lift Workshop and serves as co-chair of the SPE Forum on Production Issues in Unconventional Resources.
FAQ
DESIGNED FOR
Primarily intended for artificial lift, production and facilities engineers and students to enhance their knowledge base, increase technology awareness and improve the facility with different data analysis techniques applied on large data sets.
COURSE LEVEL
o Intermediate to Advanced
LEARNING OBJECTIVES
You will gain new knowledge and experience in these things:
o a set of tools and some pathways to analyze and manipulate their data in the cloud, find trends, and develop data-driven models.
o several business use cases that are amenable to data-driven workflows
o opportunity to solve a problem and tweak solution variables using a provided data set
REGISTER
Registration is now OPEN!
Ph.D. students, group and early bird registrants are eligible to DISCOUNT!
For more details and registration please send email to: register@petro-teach.com
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