Reservoir engineering is increasingly data-intensive, requiring engineers to leverage the power of modern computing for effective decision-making. This course is designed to help participants utilize Python programming to implement critical reservoir engineering techniques. With practical examples and expert guidance, attendees will learn how Python can streamline data analysis, improve accuracy, and ultimately enhance reservoir performance.

The course combines lectures, hands-on coding exercises, case studies, and collaborative problem-solving. Participants will work with real-world datasets and practical examples to reinforce their understanding, with guidance from experienced instructors to ensure skill application beyond the classroom.

Reservoir Engineers.

Production engineers.

Chemical engineers.

Drilling engineers.

Geologists and petrophysics

AL and workover engineers.

Day 1

Why Reservoir Engineers Should Use Python

Python Data as Related to Oil and Gas Industry

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

Exercises

Oil and Gas Data Reading and excel connection to python

Simple Reservoir Data Visualization.

Filtering Reservoir Data based on Wells (single or Multiple)

Cleaning and organizing historical data, with proper date time conversion.

Day 2

Visualization in depth.

Introduction to Delauny Triangular Maps

Introduction to Interactive Dashboards

What is Metrics and KPI and How to visually display them

More on visualization.

Detecting undeveloped reservoir areas by using base map with drainage bubbles.

Exercises

Subsurface Contour Mapping

Interactive Reservoir Data Dashboards.

Interactive Well Selection and Dynamic Data Filtering.

Cumulative Production Calculation

Bubble Map for Well Drainage Extent

Day 3

Introduction to Time Series Analysis using Auto Regression

Introduction to Water Cut Production

Selection based Water Cut Analysis and WOR prediction.

Introduction to Chan Plot for Water Oil Ratio Diagnostics

Flowing Material balance and Linear fitting.

Linear and Non-Linear curve fitting and coefficient calculation.

Linear Production Forecasting

Exercises

Chan Plot for Water Flooded reservoirs.

Using Auto Regression for Non empirical time series prediction (WOR, GOR, etc.).

X Plot

Flowing Material balance and Recover estimation.

Day 4

Reservoir Tank Modeling connection with Python

Controlling Tank Model using Python code.

Production Schedule Control

Well Performance evaluation based on numerical simulation

Reservoir PVT property calculation and tabular reporting and plotting using python

Exercises

Connect MBAL software to Python.

Do a 1d simulation and control the simulation using python

PVT property calculation

Day 5

Introduction to NumPy

Introduction Random data Class

Introduction to Statistical Distribution

Introduction to Pareto principle and opportunity identification.

Introduction to Control Chart and Well performance classification.

Introduction to Monte Carlo simulation.

Interactive Monte Carlo simulation and controlling the histogram

Reservoir Data Aggregation.

Production Data Aggregation (Yearly, Monthly).

Exercises

Pareto Well Analysis 80/20 rule.

Create Reservoir Data Distribution

Monte Carlo simulation on IOIP.

Creating Histograms.

Statistical Well Performance Evaluation using Control Chart.

On successful completion of this training course, PEA Certificate will be awarded to the delegates.