Machine Learning Model for Predicting Thermal Performance of High-Temperature Aquifer Thermal Energy Storage (HT-ATES) in Depleted Clastic Reservoirs

This repository contains a machine learning model and application for predicting the thermal performance of High-Temperature Aquifer Thermal Energy Storage (HT-ATES) systems in depleted clastic hydrocarbon reservoirs. The main script, app.py, must be downloaded and run locally using Streamlit to access the model’s functionality. app.py serves as the core application, loading the trained model and providing an interactive user interface for making predictions. Please note that this tool is not available for online deployment and requires a local Python environment with the necessary dependencies installed.