Intelligent Prediction Technology for Production of Tight Oil Based on Data Analysis

China is rich in tight oil resources, with a wide distribution range and a large amount of resources, making it one of the key areas for strategic replacement of future oil reserves and production. In response to issues such as strong heterogeneity of terrestrial tight oil reservoirs, difficulty in drilling high-quality oil layers, large production differences, and unclear main control factors for production capacity, a detailed analysis of dynamic and static data of production wells was conducted to analyze production performance and decline patterns. Production wells were classified according to production characteristics, and development indicators at different stages were statistically analyzed based on actual production days. Using a combination of principal component analysis and Pearson correlation coefficient, based on multiple dynamic and static data such as geological factors, fracturing factors, and development factors, and analyzing the correlation between different single and combined factors and cumulative oil production at different stages, the main control factors for different production stages of tight oil were obtained. A production capacity prediction model for tight oil fracturing horizontal wells was established based on machine learning intelligent algorithms, A production capacity evaluation and prediction technology for tight oil fracturing horizontal wells has been developed. By comparing with actual production data, the accuracy of the predicted results can meet production needs, providing a strong technical foundation for precise prediction and guidance of tight oil production in China.