Python Module:

This module will cover all Python basics to get started and feel confident in writing code and maintaining future code bases.

Module 1:

• Introduction to Python for the Oil and Gas Industry
• Python Tools and Package Options
• Data Types and Basic Python Functions
• Introduction to Data Casting
• Introduction to String and String Manipulation

Module 2:

• Data Containers (Lists and Dictionaries)
• Operations on Containers
• Introduction to Loops
• Introduction to Branching (IF Statements)
• Mixing Loops and Branching
• Introduction to User Functions

Module 3:

• Working with Arrays and Applying Mathematics to Them
• Working with Tabulated Data
• Basic Pandas Functions for Data Manipulation
• Introduction to Visualization
• EDA – Exploratory Data Analysis

Machine Learning Module

This module will introduce the trainee to machine learning concepts and various uses and implementations. In this module, users will learn how to utilize Python as a tool for various ML-related projects.

Module 4:

• Processing Tabulated Data
• Data Filtering, Cleansing, and Outlier Handling
• Imputations and Its Methodologies
• Feature Engineering
• Plotting for ML (Distribution, Pair Plots, Box, LMPlots, Heat)
• Machine Learning Workflows (Putting Everything in Place)

Module 5:

• Introduction to Machine Learning Types
• Introduction to Unsupervised Learning
• K-Means Clustering using Sklearn
• Clustering Oil Wells Based on Petrophysical Properties
• Clustering Gas Wells Based on Liquid Loading Index
• Hierarchical Clustering and Dendrogram
• Hierarchical Cluster-Based Water Cut in Oil Wells

Module 6:

• Introduction to Regression
• Basics of Regressions using NumPy Poly1d
• Introduction to Linear Regression in SKlearn
• Multivariate Regression in ML
• Regression Applied to Oil and Gas Production Prediction
• Evaluation of Regression Models
• Regression Sensitivity Analysis using OVAT
• Predicting Drilling Performance Using Multilinear Regression

Module 7:

• Introduction to Classification Problems
• Classifications as Applied to Oil and Gas Industry Problems
• Introduction to Logistic Regression
• Introduction to KNN
• Classifying Flow Stability in Oil Wells
• Comparing Decline Curve Analysis to ML Regression
• Introduction to Support Vectors (SVM)
• Predicting Geomechanical Properties using SVM
• Shale Formation Simulation Classification

Module 8:

• Introduction to Neural Networks
• Introduction to Deep Learning
• Introduction to Fuzzy Logic
• Applications of NN in the Oil and Gas Industry
• Model Optimization
• Dashboarding and ML Implementation
• Notes on OOP Approach and Production Grade Code