Visualization Study of Oil Well Fracturing Production Prediction Model Based on Weighted Hybrid Regression Algorithm

This study aims to explore visualization methods for an oil well fracturing production prediction model based on a weighted hybrid regression algorithm. The objective is to construct a mathematical model capable of accurately predicting oil well fracturing production through theoretical analysis and to enhance the model's interpretability and applicability using visualization techniques. The research primarily employs a weighted hybrid regression algorithm, which combines the advantages of multiple regression and weighted least squares to effectively address heteroscedasticity and multicollinearity issues in the data. Initially, a systematic analysis of factors influencing oil well fracturing production, such as geological conditions, fracturing parameters, and well structure, is conducted. Based on these factors, a weighted hybrid regression model is constructed. Subsequently, mathematical derivation and simulation experiments are used to validate the model's predictive accuracy and stability. Finally, visualization techniques, such as 3D graphics and dynamic simulation, are utilized to present the model’s prediction results and trends, enhancing the model’s intuitiveness and comprehensibility. The study concludes that the weighted hybrid regression algorithm demonstrates high accuracy and robustness in predicting oil well fracturing production, and visualization techniques effectively assist decision-makers and engineers in understanding and managing the oil well production process.