Popular GitHub Repositories for Crude Oil

Promotes development of ML algorithms for early detection and classification of undesirable events in offshore oil wells.

Framework for correlating two or more well logs using feature vectors generated from CNN's in Pytorch

Machine learning framework for reservoir simulation

Deep learning tools for predicting oil well data

An Ensemble DL Model Tuned with Genetic Algorithm for Oil Production Forecasting.

[MM 2023] Stroke-based Neural Painting and Stylization with Dynamically Predicted Painting Region

Official implementation of "MambaPainter: Neural Stroke-Based Rendering in a Single Step."

?️ Advanced anomaly detection system for Petrobras offshore wells using statistical control charts and machine learning on the 3W dataset. PIBIC Graduate Research 2025-2026 at CER-UFBA.

Machine Learning to predict share prices in the Oil & Gas Industry. In a nutshell, this is a quick introduction to understand the potential of data science and machine learning used in the oil industry. I have chosen to work with the stock price of a few oil companies (or oil service company) and the oil price data set as an example.

A Python package for production forecasting in oil and gas wells. Includes tools for production data analysis, decline curve analysis, time series forecasting, and industry-standard graphical templates for interpretation.

Navier-Stokes oil dynamics in a rectangular 3D tank, physics-informed neural network approach

NLP Article Trend Analysis (Oil & Gas)

Fire and Smoke Detection for Oil Industry Safety | YOLOv9

Forecast oil prices using Lineapy Python

Detect and Segment Oil Spills Using Computer Vision

This project implements an oil spill detection system using the DeepLabV3+ model with a ResNet101 backbone for semantic segmentation of aerial imagery. The system includes data preprocessing, model training, evaluation metrics, and a Gradio interface for real-time predictions.

LLM analysis example for the oil-sentiment repo using Langchain and Groq

Forecasting daily grocery demand for Corporación Favorita in the Guayas region (Jan–Mar 2014). Focus on clean data preparation, reproducible feature engineering (lags, rolling means, holidays, oil, perishables), and model explainability. Built with XGBoost, evaluated via time-based validation, and documented for transparency and portfolio use.

Building a Neural Network to predict the weekly gasoline price based on features such as crude oil prices, domestic production, consumption, domestic inflation as CPI, strategic reserves, weekly import and historic gasoline prices

Production-ready ML project: Forecasting GCC crude oil production with FastAPI, Streamlit, MLflow, and Docker. Features time-series modeling, SHAP explainability, and complete MLOps pipeline.