Diploma for Machine Learning in Oil & Gas Last Updated: 2 months ago Mentor: Edvantage Learning
Diploma for Machine Learning in Oil & Gas
₹19000/ $275 ₹20000/ $290
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About the course:
The training objective of a Diploma in Machine Learning for Oil & Gas is to equip students with the necessary knowledge and skills to apply machine learning techniques specifically in the context of the oil and gas industry. The program aims to provide a solid foundation in both machine learning concepts and their practical applications within the oil and gas sector.

Course Objective:

  • Understanding the fundamentals of machine learning: Students will learn the basic principles, algorithms, and techniques of machine learning, including supervised and unsupervised learning, regression, classification, clustering, and dimensionality reduction.
  • Knowledge of oil and gas industry domain: The program will provide students with an understanding of the specific challenges, opportunities, and data characteristics relevant to the oil and gas industry. This includes exploring various data types such as seismic data, well logs, production data, and reservoir data.
  • Data preprocessing and feature engineering: Students will learn how to preprocess, clean, and transform oil and gas data for use in machine learning models. They will gain expertise in feature engineering techniques to extract meaningful features from raw data.
  • Model selection and evaluation: The program will cover different machine learning algorithms and techniques suitable for oil and gas applications. Students will learn how to select appropriate models, optimize hyperparameters, and evaluate model performance using relevant metrics.
  • Predictive modelling and anomaly detection: Students will be trained to build predictive models to forecast production, optimize drilling operations, and detect anomalies, and identify potential risks in the oil and gas industry using machine learning techniques.
  • Data visualization and interpretation: The program will emphasize data visualization techniques to effectively communicate insights and patterns extracted from machine learning models. Students will learn to interpret and present results to domain experts and stakeholders.
  • Ethical considerations and industry best practices: The diploma program will address ethical considerations and challenges related to using machine learning in the oil and gas industry. It will also cover industry best practices for data handling, model deployment, and ongoing model monitoring and maintenance.

Skills & Benefits,
You will acquire:

  • High Demand and Career Opportunities
  • Solving Complex Problems
  • Automation and Efficiency
  • Data-Driven Decision Making
  • Personalization and Customer Experience
  • Improved Efficiency in the Oil & Gas Industry
  • Innovation and Research 

Who can join the course:

  • Machine learning in the oil and gas industry can be learned by various professionals who work in or are interested in the field.
  • Oil and gas professionals & students who want to start their careers in machine learning.

Prerequisites:

  • Will teach from scratch, so no prerequisites as such are required.
  • Having knowledge of mathematics, statistics, programming, and linear Algebra will be an advantage.
  • Basic knowledge of the oil and gas industry is beneficial.

Duration: 70+ hours

Important Business Benefits of the Machine Learning for the Oil & Gas industry:

  • Cost Reduction:
    Machine learning can help identify inefficiencies, optimize processes, and reduce operational costs. By analyzing large volumes of data, machine learning algorithms can identify areas for cost reduction, such as optimizing drilling operations, minimizing equipment downtime, and improving energy efficiency. These cost savings can have a significant positive impact on the bottom line of oil and gas companies.
  • Increased Productivity and Efficiency:
    Machine learning techniques enable the automation and optimization of various tasks and processes. By automating repetitive and time-consuming tasks, professionals can focus on more strategic and high-value activities. Machine learning can also optimize production rates, enhance asset management, and streamline decision-making processes, leading to increased overall productivity and operational efficiency.
  • Enhanced Predictive Capabilities:
    Machine learning models can analyze historical data and generate accurate predictions for various scenarios. This capability is particularly valuable in the oil and gas industry, where accurate predictions of production rates, equipment failures, and reservoir performance can have a significant impact on business planning and resource allocation. By learning machine learning, companies can gain a competitive edge by making informed decisions based on reliable predictive models.
  • Improved Safety and Risk Management:
    Machine learning can contribute to enhanced safety protocols and risk management in the oil and gas industry. By analyzing real-time sensor data and historical incident data, machine learning algorithms can detect anomalies, predict potential safety incidents, and identify areas of risk. Proactive identification and mitigation of safety risks can minimize accidents, protect workers, and reduce potential liabilities.
  • Enhanced Asset Management:
    Machine learning techniques can optimize asset management by predicting equipment failures, optimizing maintenance schedules, and identifying performance bottlenecks. By leveraging machine learning algorithms, companies can extend the lifespan of assets, reduce downtime, and improve the overall performance of their infrastructure. This leads to improved asset utilization, increased return on investment, and better resource management. 
  • Data-Driven Decision Making:
    Machine learning enables companies to make data-driven decisions based on insights extracted from large and complex datasets. By learning machine learning, businesses in the oil and gas industry can leverage these insights to optimize drilling operations, evaluate exploration prospects, and develop effective production strategies. Data-driven decision making reduces reliance on intuition and guesswork, leading to more informed and accurate decisions.
  • Competitive Advantage and Innovation:
    Adopting machine learning techniques in the oil and gas industry can provide a competitive advantage. By leveraging advanced analytics and machine learning algorithms, companies can gain valuable insights, identify market trends, and develop innovative solutions. This enables them to stay ahead of the competition, improve operational efficiency, and capitalize on emerging opportunities in the industry.

Agenda:

Python Fundamental

Module 1

  • Why Data Science/AI ML in Oil and Gas industry
  • Introduction to python
  • IDE (Integrated development environment)
  • Installations

Module 2

  • The python program
  • Data Types in python
  • Variables in python
  • Mathematical Operations

Module 3

  • Introduction to data structures
  • Lists: storing data
  • Tuples
  • Dictionaries

Module 4

  • If-else condition blocks
  • While Loop
  • For loop
  • Iterables 

Python Intermediate and Advance

Module 5

  • Functions: Reduce your work
  • Making your own python modules
  • Exception Handling

Data Analytics Libraries

Module 6

  • NumPy: Numerical Python
  • Arrays
  • Reshape, resize, play around the data
  • Synthetic Data Generation

Module 7

  • Pandas: read, process, and manipulate tabular data
  • Data Frames
  • Introduction to Data Visualization: Matplotlib

Module 8

  • Hands on python mini projects: Pressure Profile, Klingenberg Effect
  • Deployment of an application on web server 

Statistics and Machine Learning

Module 9

  • Introduction to statistics
  • Introduction to machine learning
  • Linear Regression, Logistic Regression
  • Tree based ML Algorithms
  • End-to-end ML Project

Deep Learning

Module 11

  • Introduction to deep learning
  • ANNs, RNNs & CNNs
  • Hands on Project using neural networks
  • Computer Vision Project

Power BI

Module 12: Data Loading

  • Data Connection
  • Field Data preparation and transformation

Module 13: Exploratory Data Analysis

  • Dealing with Distribution and outliers in field data
  • Exploring relationships between field variables

Module 14: Trend Analysis for Field Data

  • Analyzing trends for field production data
  • Analyzing field performance on field basis
  • Analyzing field performance on well basis
  • Decomposition trees

Module 15: Field Reports and Dashboards

  • Multi-well visualization
  • Building Interactive Production dashboards
  • Building real time reservoir monitoring dashboards