Course Schedule

Description

Data science is transforming the oil and gas industry by enabling data-driven decisions that optimize operations, reduce risks, and enhance profitability. This course focuses on applying data science techniques to regional oil and gas field studies using R and SQL. Participants will learn how to handle and analyze complex datasets, develop predictive models, and gain actionable insights to support exploration and production activities. This training is ideal for professionals seeking to leverage data analytics for strategic decision-making in the energy sector.

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Course Description

Introduction


Data-driven approaches are becoming essential in the oil and gas industry to maximize efficiency and uncover opportunities. This course equips participants with the knowledge and skills needed to apply data science methods to regional oil and gas field studies. Through hands-on exercises and real-world examples, you will explore how to utilize R and SQL to analyze geological and production data, generate predictive models, and make data-backed decisions that improve operational outcomes.

Objectives


  • Understand the fundamentals of data science in the context of oil and gas field studies.
  • Learn how to use R and SQL to analyze geological and production data.
  • Develop predictive models to identify trends and optimize exploration strategies.
  • Gain the ability to visualize data and communicate insights effectively.
  • Apply data science techniques to solve industry-specific challenges.
  • Enhance decision-making processes using data analytics.
  • Training Methodology


    The course employs a practical, hands-on training approach that blends theoretical knowledge with interactive sessions. Participants will engage in real-life case studies, guided exercises, and collaborative projects that focus on solving problems specific to oil and gas exploration. The use of R and SQL will be emphasized, with step-by-step demonstrations and practice sessions to ensure a deep understanding of data science applications.

    Organisational Impact


  • Develop a data-driven culture within your organization for enhanced decision-making.
  • Improve exploration and production strategies with precise data analytics.
  • Reduce operational risks by using predictive models and data insights.
  • Optimize resource allocation and project management through better data interpretation.
  • Foster innovation by integrating advanced data science techniques into business processes.
  • Personal Impact


  • Gain valuable skills in data science that are highly relevant to the oil and gas industry.
  • Enhance your analytical capabilities with practical knowledge of R and SQL.
  • Increase your marketability and career growth potential in the energy sector.
  • Build confidence in making data-backed decisions for exploration and production.
  • Develop the ability to tackle complex industry challenges with innovative solutions.
  • Who Should Attend?

  • Geologists and Geoscientists looking to enhance their data analysis skills.
  • Petroleum Engineers seeking to integrate data science into exploration and production.
  • Data Analysts and Data Scientists aiming to specialize in oil and gas applications.
  • Technical professionals in the oil and gas sector involved in decision-making processes.
  • Anyone interested in learning how to apply data science in the energy industry.
  • Course Outline


    Day 1: Introduction

    Introduction important aspects of reservoir characterization and all data

    Introducing industrial datasets from different fields such as well log data, core data, pressure data, production, drilling, and pore pressure.

    Introduction of data science approach in data analytics from scratch

    Introduction R programming 

    Introduction of SQL role in database management 

    Brief introduction of RStudio

    Brief introduction of various R Packages

    Introduction of different applications of data analytics in upstream

    Introduction of data analytics steps by R



    Day 2: Data analytics

    Data importing, data appending, data transforming, handling missing data, 

    Data Wrangling such as wells data Joining, Combining, and Reshaping

    Well-log data Aggregation and Group Operations

    Confidently use R to solve different reservoir parameters such as porosity, water saturation evaluation in sandstone and carbonate reservoirs, different approaches in shale evaluation, logs relationship, and representing various cross-plot

    Easily create high-quality visualization of different well data 

    Visualizations I



    Day 3: Database computation 

    Preparation of field data management by SQL

    Database organization, SELECT, TABLES, FILTERING, JOINING, AGGREGATION… with well-log data by SQL

    Connecting R and SQL to extract useful data from the database

    Core Analytics (FZI investigation in multi-wells)

    Advanced Visualizations II



    Day 4: Report/Dashboard publishing

    Web scrapping to import live data from a website to R and Store in the Database

    Preparing informative dashboard from well data analysis in R Markdown

    Publishing report/dashboard on the internet



    Day 5: Fundamentals of Machine Learning

    Introduction of Machine Learning approaches in the oil and gas industry

    Preparing raw data to use in ML algorithm (Regression method)

    Run the actual ML Stream from scratch (Regression method)

    How to evaluate and select the best ML approach

    Certificates


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

    About The Trainer
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      Experienced Energy Professional: Over 19 years of expertise in roles including Geoscientist, Petrophysicist, Drilling/Reservoir Geomechanics, and Renewable Data Scientist, with a career spanning Iran, Oman, Malaysia, China, Austria, Turkey, and New Zealand.


        Data Science and Machine Learning Specialist: Skilled in applying data science techniques using Python, SQL, R, Power BI, and other tools to forecast attributes like inflow rates, wind speed, and solar radiation in the energy industry.


          Strategic Role in Renewable Energy: Member of the analytics team at Genesis Energy, focused on strategic planning for renewable projects, decarbonization, and sustainability in the wholesale electricity market.


            Advocate for Energy Transition: Involved in subsurface studies for underground gas storage and carbon capture, and actively engaged in teaching advanced courses on machine learning applications in energy transition and renewable energy data. Passionate about New Zealand's renewable energy development, CCS, and hydrogen storage potential.