Decision Support Algorithm for Fine Parameter Adjustment of Oil Production Wells in Oilfield Combining Big Data Analysis and Reinforcement Learning

The purpose of this study is to explore a decision support algorithm for fine parameter adjustment of oil production wells, which integrates big data analysis and reinforcement learning (RL), and to evaluate its performance in optimizing the production efficiency and parameter adjustment efficiency of oil production wells. By combining the advantages of big data analysis and RL, this paper puts forward a novel algorithm, aiming at automatically adjusting the operating parameters to maximize oil production or minimize production costs. In this study, firstly, the historical data and real-time data of oil production wells are analyzed by big data to explore the relationship between operating parameters and production results. Then, the decision-making model is established by using RL technology, and the optimal operation strategy is selected according to the results of data analysis. This paper designs a series of experiments to verify the effectiveness and performance of the algorithm, including comparing the differences between the fusion algorithm and the traditional rule-based parameter adjustment method and the big data analysis method alone. The experimental results show that the fusion algorithm has obvious advantages in optimizing the production efficiency of oil production wells and improving the efficiency of parameter adjustment. This study provides new ideas and methods for intelligent management and sustainable development of oil production engineering in oil fields.