An Interactive 5-Day Training Course
Python for Production Engineering
Coding for Production Workflows
Secure your spot now!
Duration
5 Days
Price
$ 2,000
Starting From
09 Aug 2027
Python for Production Engineering
Have Questions ?
Python for Production Engineering - PE-PYP-PEA27
| Code | Date | Time | Duration | Location | Currency | Early Bird Fee Per Person |
|---|---|---|---|---|---|---|
| PE-PYP-PEA27 | 09 - 13 Aug 2027 | 10 AM CST | 4 Hours Per Day |
Online |
USD |
2000 |
Need this for a group? We deliver the same course in-house — face-to-face at your location or online — tailored to your assets and team level. Contact info@peassociations.com.
Boost your team's skills and your budget! Enjoy group discounts for collaborative learning. Send an inquiry to info@peassociations.com.
Python for Production Engineering
Description
Production engineers spend a large share of their time moving data between systems, cleaning it, plotting it and repeating the same calculations. Python removes most of that repetition. It reads production databases and spreadsheets, cleans and validates data, performs the standard production engineering calculations, produces plots and reports, and runs the whole workflow again tomorrow without further effort.
This course teaches Python through production engineering work rather than through generic programming exercises. It starts with Python fundamentals and the scientific libraries, then covers reading and handling production data with pandas, data cleaning and validation, and plotting. It then builds production engineering calculations in code: PVT correlations, inflow performance, tubing performance and nodal analysis, decline and rate transient analysis, and artificial lift data analysis including dynamometer and ESP data. The course closes with automating routine reporting, structuring reusable code, version control and sharing work with colleagues.
Most production engineering calculations are short, well-defined and repeated constantly: convert units, apply a PVT correlation, build an IPR, calculate a pressure traverse, fit a decline curve, flag wells outside their expected range. Doing these in a spreadsheet works for one well and breaks down at a hundred. Doing them in Python works the same for one well or a thousand.
Data handling is where most of the benefit appears. Production data arrives from several systems in inconsistent formats with gaps, duplicates and errors. A short Python script that reads the sources, aligns them, applies validation rules and flags exceptions replaces hours of manual work every week, and does it consistently.
Nothing in this course requires prior programming experience. The aim is not to produce software developers but engineers who can write, read and modify practical scripts, automate their own routine work and understand the code used by others in the organisation. Everything is built around production engineering tasks that participants already do by hand.
By the end of this training, participants will be able to:
- Write Python programs using core language features and standard libraries.
- Read, combine and handle production data using pandas.
- Clean, validate and flag errors in production data sets.
- Produce plots and automated reports from production data.
- Implement PVT and fluid property correlations in code.
- Build inflow, tubing performance and nodal analysis calculations.
- Perform decline curve and rate transient analysis programmatically.
- Analyse artificial lift data, including dynamometer and ESP records.
- Automate routine reporting and surveillance tasks.
- Structure, document, version and share reusable code.
Organisations sending participants to this training will:
- Automate repetitive production engineering and reporting work.
- Improve consistency and auditability of routine calculations.
- Handle large well populations without proportional effort.
- Improve data validation and error detection.
- Build internal capability that reduces dependence on external tools.
Participants will:
- Be able to write and modify practical Python scripts.
- Be able to handle and clean production data efficiently.
- Be able to implement production engineering calculations in code.
- Be able to automate reporting and surveillance tasks.
- Be prepared for machine learning and data analytics work.
- Production engineers
- Reservoir and well surveillance engineers
- Production technologists
- Data and reporting staff supporting production
- Operations engineers handling large data sets
- Graduate engineers in technical roles
Module 1: Python Fundamentals
- Environment setup and notebooks
- Variables, types and operators
- Control flow and loops
- Functions and modules
- Errors and debugging
Module 2: Scientific Python
- NumPy arrays and vectorised calculation
- Units and conversions
- Numerical methods: interpolation, root finding and integration
- Working with dates and time series
Module 3: Production Data Handling
- Reading spreadsheets, CSV files and databases
- DataFrames and indexing with pandas
- Merging and reshaping data
- Resampling and aggregation
- Handling missing data
Module 4: Data Cleaning and Validation
- Detecting outliers and impossible values
- Validation rules for production data
- Flagging and reporting exceptions
- Reconciling data from multiple sources
- Building a reusable cleaning pipeline
Module 5: Plotting and Reporting
- Plotting production trends and diagnostics
- Multi-well and multi-panel plots
- Log-log and specialised engineering plots
- Automated report generation
- Exporting results
Module 6: Fluid Properties and Inflow
- Implementing PVT correlations
- Black oil property calculations
- Oil and gas inflow performance relationships
- Building IPR curves in code
Module 7: Well Performance Calculations
- Pressure traverse calculations
- Multiphase flow correlations in code
- Nodal analysis and operating point solution
- Sensitivity studies across many wells
Module 8: Decline and Lift Analysis
- Decline curve fitting
- Rate transient analysis calculations
- Forecasting across a well population
- Dynamometer and ESP data analysis
- Detecting anomalies automatically
Module 9: Automation and Good Practice
- Structuring scripts and reusable functions
- Scheduling and running jobs automatically
- Version control basics
- Documentation and code review
- Sharing tools with colleagues
Upon successful completion of this training course, delegates will be awarded an official Certificate of Completion issued by the Petroleum Engineers Association (PEA), an ISO 9001:2015 certified training organization. The certificate carries 10 Credits and formally records the total learning hours completed.
Each certificate is signed by the Course Facilitator and the CEO of the Petroleum Engineers Association, and serves as verifiable proof of professional training that delegates can present to employers and professional bodies worldwide.
Frequently Asked Questions
All course bookings made through PEA are strictly non-refundable. By registering for a course, you acknowledge and accept that all fees are payable in full and are not subject to refund under any circumstances, including changes in personal or professional commitments or partial attendance.
PEA reserves the right to make reasonable adjustments to course content, trainers, or schedules where necessary, without entitling delegates to a refund. Comprehensive details of each course — including objectives, target audience, and content — are clearly outlined before enrolment, and it is the responsibility of the delegate to ensure the course's suitability prior to booking.
For any inquiries related to cancellations or bookings, please contact our support team, who will be happy to assist you.
