Course Overview 5/5

This course aims at providing students with a detailed overview of Python and its libraries, with a focus on its application in Well logging. The student will get to do EDA, feature engineering, outlier detection, predicting of DTC, DTS & geomechanical logs. Technical aspects of both python & Domain are explained at greater depths.

Topics Covered

What is Machine Learning

Feature Engineering

Projects you will build

Supervised Lithology Classification

Using AI/ML tools to identify facies

Artificial Intelligence

Supervised Lithology Classification

EDA, Feature Engineering, Volve Dataset

Data Driven Sonic Well Log Model

Machine-learning techniques to predict DTC and DTS logs to improve subsurface characterization.

Artificial Intelligence

Data Driven Sonic Well Log Model

Supervised Learning, Classification

Machine Learning,Outlier Detection

Well Log Visualization using Python

Course Overview 5/5

Topics Covered

Course Curriculam

Module 1: Getting Data (Volve Dataset) 2 hrs.

Module 2: Data Visualization 10 hrs.

Module 3: Exploratory Data Analysis 10 hrs.

Module 4: Feature Engineering 5 hrs.

Module 5: Outlier Detection 2 hrs.

Module 6: Filling NaN values 1 hrs.

Module 7: Flagging Bad Hole (based on logs) 4 hrs.

Module 8: Predicting DTC, DTS log 4 hrs.

Module 9: Predicting Geomechanical Logs 4 hrs.

Module 10: Supervised Facies Classification 4 hrs.

Module 11: Unsupervised Facies Classification 4 hrs.

Pick a batch

1st Apr - 15th Jun
Mon, Tue, Wed, Thu, Fri
07:00 - 08:00PM

Projects you will build

Supervised Lithology Classification

Data Driven Sonic Well Log Model

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.