Day 1

Topics:

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

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 datetime conversion

Day 2

Topics:

Introduction to Clustering

Understanding Distance Metrics

Introduction to Scikit-Learn Library for ML

Anomaly Detection Techniques

Exercises:

Clustering water production data

Detecting anomaly in production data

Day 3

Topics:

Introduction to Classification

Voting and Decision Trees

KNN Method

Decision Tree and Random Forest

Python Plotting Techniques

Exercises:

Classifying ESP operational problems

Predicting flow regime type

Day 4

Topics:

Introduction to Continuous Data and Relationships

Visualization & Correlation Matrix

Regression Analysis

Linear Regression Fundamentals

Support Vector Regression (SVR)

Xtreme Gradient Regression (XGBoost)

Exercises:

Training ML to behave like PROSPER software

Predicting hydrocarbon properties using ML

Day 5

Topics:

Introduction to Time-Bounded Data in Oil & Gas

Decline Curve Analysis (DCA) and Limitations

Introduction to Time Series Analysis (TSA)

Time Series Data Patterns & Components

Exercises:

Predicting shale production decline using AR models

Predicting water cut based on WHP, Qo, Qg

Day 6

Topics:

Python Programming Refresher

Data Types and Structures

Visualization with Pandas

Data Cleaning and Transformations

Exporting Data

Exercises:

Manipulation and analysis of production, reservoir, and petrophysical data

Day 7

Topics:

Introduction to Unsupervised Learning

Clustering Concepts

Distance Metrics

Scikit-Learn for ML

Anomaly Detection Methods

Local Outlier Factor with Time Window

Exercises:

Clustering petrophysical data

Anomaly detection in production data

Dashboarding for clustering and opportunity identification

Day 8

Topics:

Introduction to Supervised Learning

Data Types & Classification

Labels and Events in Oil & Gas

Decision Trees & Related Algorithms

Pandas DataFrames for Classification

Binary & Multiclass Classification

Decision Maps

Exercises:

Classifying formation layers from well logs

Predicting asphaltene deposition possibility

Rock type identification using mineral analysis

Day 9

Topics:

Continuous Data & Correlations

Linear Relationships

Regression Techniques

Linear Regression

Support Vector Regression

XGBoost Regression

Exercises:

Training ML as a numerical simulator

Predicting PVT properties

Predicting water cut from KPIs

Predicting petrophysical properties from LAS files

Day 10

Topics:

Time-Bounded Data in Oil & Gas

Decline Curve Analysis vs Time Series Analysis

Time Series Data Patterns

Simple & Exponential Moving Averages

AR and ARIMA Models

Exercises:

Predicting oil decline using AR models

Predicting water cut from WHP & oil rate

Multi-well data fitting with AR models

Reservoir pressure forecasting