Survey on Machine Learning Models for Oil Production Forecasting

This survey provides a detailed review of artificial intelligence and machine learning methods used for oil production forecasting. It examines different models, their strengths, limitations, and performance comparisons. Traditional models like Multiple Linear Regression (MLR) and ARIMA struggle with capturing complex patterns, while advanced techniques such as Random Forest (RFR), Decision Tree (DTR), XGBoost, and Artificial Neural Networks (ANN) show better accuracy. ANN performs the best overall but requires high-quality data and significant computing power. Hybrid models that combine different techniques offer promising improvements but face challenges like high computational costs and complex tuning. Future research should focus on making these models more efficient, easier to use, and better suited for real-world applications. AI-driven forecasting can greatly improve decisionmaking and resource management in the oil and gas industry.