Course Objectives: The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making.

Data analytics is essential to delivering unconventional drilling results in a cost-efficient and safe manner. Drilling companies must develop modern tools for gathering and organizing real-time data from operations—enabling monitoring algorithms, predictive analysis, risk reports, safety alerts, and more.

Duration: 20+ Hours

Prerequisites:  This Course is designed for student and professional.

Learning Objectives:

• Continuous job Cuts and lack of job opportunities in Oil & Gas sector
• Increasing Opportunities in Oil & Gas Companies
• To increase work efficiency by using data science application
• Attractive Salary for Entry/ Experienced Level Engineers

Benefits of Joining The Program

• Flexible & blended learning model : Learn with the convenience and flexibility of recorded and live sessions. Because most working professionals want to participate in the program, live sessions will only be held on weekends.
• Discussion Forum : Participants will get a discussion forum after joining the program where they can ask their questions directly to faculty.
• Hands on experience on Oil & Gas Projects : A variety of different Oil & Gas case studies and projects discussed to provide clear understanding of the used cases in Oil & Gas Industry.
• No Prior coding experience : Coding is not required since we are starting from scratch.

Applications of Data Science in Oil & Gas

• Analyzing seismic and micro-seismic data
• Improving reservoir characterization and simulation
• Reducing drilling time and increasing drilling safety
• Improved petrochemical asset management
• Optimization of the performance of production pump
• Improved shipping and transportation 

Current Updates Of Data Science In Oil & Gas

• More than 400,000 oil and gas sector jobs have been cut this year, according to Rystad
• Over more than 200k recently petroleum graduated are looking for job
• ~53% of workforce concerned about job security
• Less than 15% of OG&C employees are data analytics/mathematic majors
• As per the recent Report by 2025 the demand of data science & Machine skills is expected to drive 27.9% increment in employment ~53% of workforce concerned about job security.

Course Journey

Statistics

• Sample or Population Data?
• The Fundamentals of Descriptive Statistics
• Measures of Central Tendency, Asymmetry, and Variability Lesson
• Practical Example: Descriptive Statistics
• Distributions
• Estimators and Estimates
• Practical Example: Inferential Statistics
• Hypothesis Testing: Introduction
• Hypothesis Testing: Let’s Start Testing!
• Practical Example: Hypothesis Testing
• The Fundamentals of Regression Analysis
• Subtleties of Regression Analysis 

Python Fundamental (by pressure, production and reservoir data)

    Module 1: Introduction to Python and Computer Programming Python

• Python a tool, not a reptile
• There is more than one Python
• Let's start our Python adventure

    Module 2 - Data Types, Variables, Basic Input-Output Operations, Basic Operators

• Your first program
• Python literals Operators 
• data manipulation tools Variables
• data-shaped boxes
• How to talk to computer

    Module 3 - Boolean Values, Conditional Execution, Loops, Lists and List Processing, Logical and Bitwise       Operations

• Making decisions in Python
• Python's loops
• Logic and bit operations in Python
• Lists - collections of data
• Sorting simple lists - the bubble sort algorithm
• Lists - some more details
• Lists in advanced applications

    Module 4 - Functions, Tuples, Dictionaries, and Data Processing

• Writing functions in Python
• How functions communicate with their environment?
• Returning a result from a function Scopes in Python
• Let's make some fun... sorry, functions
• Tuples and dictionaries

    Module 5 - Modules, Packages, String and List Methods, and Exceptions

• Using modules
• Some useful modules
• What is package?
• Errors - the programmer's daily bread
• The anatomy of exception
• Four simple programs 

Data Analytics for Oil & Gas (Python and Power BI)

• Introduction different well and core data in real oil and gas fields
• Introduction of different steps of reservoir characterization
• Introduction of data analytics application in upstream 
• Confidently use Python to solve different RESERVOIR parameters such as, porosity, water saturation evaluation in sandstone and carbonate reservoirs, different approach in shale evaluation, logs relationship, and representing various cross plot such as Pickett plot
• Introduction of SQL role in database management
• Easily create high-quality visualization of different well data such as NPHI, GR, RHOB, DT…to simplify the data analysis and get ready-to- apply plots for visualization by python or power BI
  
• Application of M-languages and DAX in formation evaluation analysis
• Python in SQL and Python
• Brief introduction of Jupiter Notebook and python programming
• Brief introduction of Power BI application in Data analytics
• Getting familiar with different query editor in Power BI
• Preparing informative dashboard from well data analysis
• Log correction, shale evaluation, working with different cross plot, porosity and water saturation in carbonate and sandstone reservoir

Machine Learning

• Introduction to Artificial Intelligence and Machine Learning
• Well Data Preprocessing
• Supervised Learning in reservoir parameters estimation such as water saturation and compressibility factor
• Feature Engineering for porosity estimation
• Supervised Learning-Classification
• Unsupervised Learning, in facies analysis
• Time Series Modelling
• Ensemble Learning in net/pay zone detection Recommender Systems

Data Science & Machine Learning Projects

The basic idea is to introduce participants the concepts of exploratory data analyses, machine learning workflows and most importantly, data analytics and machine learning use cases in Oil & Gas applications. A variety of case studies will be discussed in order to provide attendees a better grasp of the applicability.

Data Science Research Project

• Comparison between Vogel's and Fetkovich's Model for IPR
• Effect of Skin on Bottom Hole Pressure & Future IPR
• Gas Material Balance
• MBAL Oil & Spinner Log Analysis
• The Klinkenberg Effect
• Vogel's IPR & Well Test
• 1D Pressure Diffusion Numerical Solution
• 1D infinite domain Analytical to do
• 2 Phase RelPerm Models
• All Regimes WTA
• Reservoir Heterogeneity & Pressure Profile
• Reservoir Simulation 1D
• Single Phase Flow Simulator 1
• Thermal Stimulation & Well Test Analysis
• Well log Interpretation & Drilling Optimization
• Computational Python SymPy
• Constant Pressure Boundary
• Dead Oil Well Test EDA