The oil and gas sector generates massive volumes of data daily. Data mining skills allow professionals to turn this data into a powerful asset, supporting everything from resource management to predictive maintenance. In this comprehensive course, participants will gain an understanding of the methods and tools essential for extracting meaningful patterns from data to improve operational efficiency and make data-informed decisions.

The course combines interactive lectures, case studies, hands-on exercises, and group discussions, allowing participants to learn both theoretical concepts and practical applications. Real-world oil and gas scenarios are included to enhance relevance and ensure applicability.

Reservoir Engineers.

Production engineers.

Chemical engineers.

Drilling engineers.

Geologists and petrophysics

AL and workover engineers.

Any one with exposure to Large Volumes of Data

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