Machine Learning-Based Prediction of Thermal Properties of Sedimentary Rocks from Well-Log Data

Abstract

In this study, models are developed that predict for sedimentary rocks (clastics, carbonates and evapourates) thermal properties comprising thermal conductivity, specific heat capacity and thermal diffusivity.

Keyword: Thermal conductivity; Thermal diffusivity; Heat capacity; Machine learning; Well-logging downhole methods; Sedimentary basin

Guide

If have problem with load model
1- Create virtual environment (python 3.9.12):
python -m venv thermal-venv
thermal-venv\Scripts\activate
2- Install below versions library:
pip install pandas
pip install numpy
pip install joblib==1.2.0
pip install matplotlib==3.8.1
pip install scikit-learn==1.2.1
pip install xgboost==1.6.1
3- Add venv to ipykernel (if use jupyter notebook)
pip install ipykernel
python -m ipykernel install --user --name=thermal-venv

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${\color{red} 1-Log}$
1- ['RHOB']
2- ['PHIN']
3- ['VSH']
4- ['Vp']

${\color{red} 2-Log}$
5- ['RHOB', 'PHIN']
6- ['RHOB', 'VSH']
7- ['RHOB', 'Vp']
8- ['PHIN', 'VSH']
9- ['PHIN', 'Vp']
10- ['VSH', 'Vp']

${\color{red} 3-Log}$
11- ['RHOB', 'PHIN', 'VSH']
12- ['RHOB', 'PHIN', 'Vp']
13- ['RHOB', 'VSH', 'Vp']
14- ['PHIN', 'VSH', 'Vp']

${\color{red} 4-Log}$
15- ['RHOB', 'PHIN', 'VSH', 'Vp']

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