Module 1

 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.

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.

Module 2

 Introduction to Exploratory Data Analysis

 Importance of EDA in the data science workflow

 Overview of the EDA process: understanding the dataset, data cleaning, and initial insights

 Tools and libraries for EDA: Pandas, Matplotlib, Seaborn, and Plotly

 Understanding the Dataset

 Exploring dataset structure: rows, columns, data types

 Inspecting the first and last few rows, and summary statistics

 Identifying missing values, duplicates, and outliers

 Descriptive statistics: mean, median, mode, variance, standard deviation

 Visualizing Data Distributions

 Visualizing the distribution of numeric variables: histograms, boxplots, and density plots

 Exploring categorical data: bar charts, pie charts, and count plots

 Using pair plots and scatter matrix plots for feature relationships

 Identifying skewness, kurtosis, and data transformation needs

Exercises

• Exercise 1: Dataset Overview

• Load a dataset and explore its basic structure using head (), tail (), and info ().

• Calculate and interpret summary statistics such as mean, median, and standard deviation.

• Identify missing values and duplicates in the dataset and apply strategies to handle them.

• Exercise 2: Visualizing Data Distributions

• Create histograms and boxplots to visualize the distribution of numerical features.

• Generate bar charts and count plots for categorical variables to understand their frequency distribution.

• Identify skewness in the data and suggest appropriate transformations.

Module 3

 introduction to Relations and Correlation

 Understanding relationships between variables in data

 Difference between correlation and causation

 Types of relationships: linear, non-linear, monotonic, and non-monotonic

 Introduction to Pearson’s correlation coefficient and other correlation measures

 Exploring Correlation in Data

 Interpreting correlation coefficients (positive, negative, zero correlation)

 Visualizing correlations: heatmaps, scatter plots, and pair plots

 Understanding and handling multicollinearity

 Exploring other correlation methods: Spearman's rank, Kendall’s Tau

 Data Fitting

 Introduction to data fitting and its importance in data science and machine learning

 Overview of different data fitting techniques: linear regression, polynomial fitting, and spline fitting

 Fitting a line to data: simple linear regression model

 Exploring advanced data fitting techniques: multiple regression, non-linear regression, and curve fitting

 Model evaluation: R-squared, Mean Squared Error (MSE), and residual analysis

Exercises

• Calculate and interpret the Pearson correlation coefficient between numerical features in a dataset.

• Visualize correlations using a heatmap and scatter plots.

• Identify highly correlated variables and discuss the implications of multicollinearity.

• Use Spearman's rank correlation to analyze the relationship between ordinal variables.

• Compare Pearson and Spearman correlation methods using different types of datasets.

• Create scatter plots to visualize linear and non-linear relationships between features.

Module 4

 Introduction to Document Data Mining

 Overview of document data mining and its significance

 Types of documents: structured vs. unstructured data

 Key techniques used in document data mining: text preprocessing, feature extraction, and model building

 Applications of document data mining in various industries: sentiment analysis, topic modeling, and text classification

 Text Preprocessing

 Text cleaning: removing stop words, punctuation, and irrelevant characters

 Tokenization: breaking down text into words or phrases

 Stemming and Lemmatization: reducing words to their root form

 Handling case sensitivity and text normalization

Exercises

• Reading Full Folders Containing PDFs.

• Extract PDF data from Drilling Reports.

• Extract Patterns from Text Data.

• Search in Documents

Module 5

 Introduction to the Concept of classification

 Voting and Decision Trees

 Introduction to KNN method

 Introduction to the Decision Tree and Random Forest Methods

 Python Plotting techniques

Exercises

• Classifying ESP Operational Problems.

• Predicting Flow Regime Type

Module 6

 Introduction to Continuous Data and Corresponding Relationships

 Relationship Visualization and Correlation Matrix

 Introduction to Regression Analysis

 Linear Regression Fundamentals

 Support Vector Regression (SVR)

 Xtreme Gradient Regression (XGBoost Library)

Exercises

• Training ML to Behave like PROSPER software

• Predicting Hydrocarbon Properties using ML

Module 7

 Introduction to Time-Bounded Data in the Oil and Gas Industry

 Understanding Typical Decline Curve Analysis (DCA) and Its Limitations

 Introduction to Time Series Analysis (TSA)

 Short-Term Production Prediction Using Time Series Analysis

 Simple Moving Average (SMA) and Exponential Moving Average (EMA)

 Introduction to Auto Regressive (AR) Models

Exercises

• Predicting Shale Production Decline using Auto Regression Models.

• Predicting Water Cut Based on WHP, Qo, Qg

Module 8

 Introduction to Unsupervised Learning

 Overview of unsupervised learning and its applications in data analysis

 Key concepts: no labeled data, finding hidden structures, and pattern recognition

 Unsupervised learning tasks: anomaly detection, similarity discovery, and clustering

 Anomaly Detection

 Definition and importance of anomaly detection in various industries (e.g., fraud detection, network security, etc.)

 Types of anomalies: point anomalies, contextual anomalies, and collective anomalies

 Techniques for anomaly detection: statistical methods, distance-based methods, and density-based methods

 Common algorithms: K-Nearest Neighbors (KNN), One-Class SVM, Isolation Forest, Local Outlier Factor (LOF)

Exercises

• Exercise 1: Anomaly Detection with KNN and LOF

• Apply K-Nearest Neighbors (KNN) to detect point anomalies in a given dataset.

• Use Local Outlier Factor (LOF) to identify dense regions of anomalies in a dataset.

• Evaluate the performance of both techniques using precision, recall, and F1-score.

• Exercise 2: Similarity Discovery with Cosine Similarity

• Calculate the cosine similarity between pairs of documents in a text corpus.

• Use similarity measures to cluster similar documents and evaluate the clusters.

• Apply dimensionality reduction techniques like PCA or t-SNE to visualize high-dimensional similarity relationships. 

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