Machine Learning,Supervised Learning

AI Specialist in Oil and Gas

Level: Advance Grades: Finished College

Machine Learning,Supervised Learning

200 Hours of LIVE instruction

Weekdays

1:1 1:5

225000.00 250000.00

10% off

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Course Overview 5/5

This course is aimed at providing in-depth application of AI/ML in the Upstream & Midstream sectors. Students will be exposed to industry problems and will gain hands-on experience by working on several use cases. It covers different topics like reservoir modelling, logging, stress analysis, 3-D visualization, wave propagation, image classification which are aimed at building the core.

Topics Covered

Creating a Machine Learning Model

Feature Engineering

Working with well logs in Python

Course Curriculam

Module 1: Model Ensembling 4 hrs.

Module 2: Expectation-Maximization algorithm 4 hrs.

Module 3: Basic Overview 2 hrs.

Module 4: Data Pre-processing 5 hrs.

Module 5: Advanced Feature Engineering 4 hrs.

Module 6: Exploratory Data Analysis 1 4 hrs.

Module 7: Introduction to deep learning 6 hrs.

Module 8: Artificial Neural Network 6 hrs.

Module 9: Mini Project 20 hrs.

Module 10: Neural Networks for Subsurface 10 hrs.

Module 11: Convolutional Neural Network 15 hrs.

Module 12: Hyperparameter Tuning 20 hrs.

Module 13: Advanced Feature Engineering Part-2 10 hrs.

Module 14: Reinforcement Learning 10 hrs.

Module 15: Artificial Lift (using ANN) 8 hrs.

Module 16: Log Analysis (using AI) 30 hrs.

Module 17: Seismic Analysis (using AI) 30 hrs.

Pick a batch

1st Apr - 31st Dec
Mon, Tue, Wed, Thu, Fri
07:00 - 08:00PM

Session 1:1st Jan	Thursday	07:00 - 08:00PM
Session 2:1st Jan	Thursday	07:00 - 08:00PM

Unsupervised Learning Python Tensorflow Numpy and Pandas OpenCV Image Preprocessing Edge Detection Signal Preprocessing Exploratory Data Analysis

Projects you will build

Supervised Lithology Classification

Using AI/ML tools to identify facies

Artificial Intelligence

Supervised Lithology Classification

EDA, Feature Engineering, Volve Dataset

Digital Twin for Sucker Rod Pump

Calculating downhole dynacard from surface dynacard and pump failure identification using automated dynacard identification.

Artificial Intelligence

Digital Twin for Sucker Rod Pump

Digital Twin, Prescriptive Maintenance

Machine Learning in Seismic Interpretation

To figure out and understand the patterns in datasets that consist of input data without labeled responses.

Artificial Intelligence

Machine Learning in Seismic Interpretation

Supervided Learning, Classification

Learn Effectively with our Industry Focused Approach

Petrocoder runs most specialised courses in oil & energy sector developed by industry specialists after several decades of experience.