Dynamic Prediction and Optimization of Tight Reservoir Development Based on Machine Learning

With the continuous rise in global energy demand, the development of tight reservoirs has become a key focus in oil and gas research. However, due to the characteristics of tight reservoirs, such as significant heterogeneity and low permeability, conventional development methods face challenges in accurately predicting dynamic changes and achieving optimized extraction. In recent years, machine learning technology, known for its robust data processing and pattern recognition capabilities, has demonstrated promising potential in addressing the complexities of tight reservoir development. This paper proposes a comprehensive solution leveraging machine learning to address the challenges of dynamic prediction and optimization in tight reservoir development. Initially, a dynamic prediction model for tight reservoirs is established, with an appropriate machine learning algorithm selected for training and validation using real-world data. Following this, an optimization algorithm is applied based on the prediction outcomes, enhancing the development strategy to improve operational efficiency and economic returns. The efficacy and suitability of the proposed approach are validated through case studies. The research presented in this paper offers innovative insights and technical support for the efficient exploitation of tight oil reservoirs.