Exploring Advanced Optimized Machine Learning Frameworks for Enhanced Analysis and Predictive Modeling of Drilling Well Production Performance

The oil and gas industry is increasingly depending on advanced data analytics to maximize production and reduce costs. Perhaps the greatest difficult issue in this industry is precisely estimating the performance of drilling wells to improve decision-making processes. This paper provides ML techniques for predicting drilling well production using ensemble learning. To develop an accurate prediction system, the framework includes Exploratory Data Analysis (EDA), data preprocessing, visualization, optimization, and model selection. In this work, combining RF with XGBoost aims to improve predictive performance through utilizing the capabilities of both algorithms. Firefly Optimization is utilized to tune hyperparameters in the hybrid model-building process. This hybrid model is specifically intended for error prediction in firefly optimization scenarios. The approach is developed in Python software and exhibits the real-world application of advanced ML techniques in industrial conditions. The proposed hybrid RF-XGBoost model exceeds in terms of error predicting for drilling well production, with an RMSE of 0.01, MAE of 0.01, and MSE of 0.10. The results indicate the approach is effectively accurate in predicting errors in complex data settings, providing insights that have a significant effect on operational decisions in the field of well data management.