Thermal-property profiles from well-logs in sedimentary rocks: a physically informed Machine-Learning based prediction tool (ThermoProfiler)

Overview

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

Examples

Find multiple examples under: https://github.com/Hamid-Reza-Mousavi/SHC-TC-TD-Prediction-using-petrophysical-well-logs/tree/main/notebook

${\color{red} 1-Log}$
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']