Python Module

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

Module 1:

·       Introduction to python for 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 container (Lists and Dictionaries)

·          Operation on containers.

·          Introduction to loops

·          Introduction to Branching (IF Statements)

·          Mixing Lops 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 concept and various uses and implementations, in this module user will learn how to utilize python as a tool for various ML related projects

Module 4:

·       Processing Tabulated Data.

·       Data Filter, cleansing and Outlier handing

·       Imputations and it’s 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-Mean Clustering using Sklearn

·       Clustering Oil wells based on petrophysical properties

·       Clustering Gas Wells Based on Liquid Loading Index

·       Hierarchal clustering and Dendrogram.

·       Hierarchal 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 Geomechanically 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 oil and gas industry

·       Model Optimization

·       Dashboarding and ML Implementation

·       Notes on OOP approach and Production Grade Code.

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