In order to meet the demand of intelligent oilfield construction for accurate management of oil well life cycle, this paper puts forward an integrated algorithm optimization and decision support method of oil well fracturing-hole filling-plugging, which integrates digital twinning and reinforcement learning (RL). Aiming at the problems of data fragmentation, response lag and multi-objective trade-off in traditional phased operation, a digital twin model based on physical drive is constructed to realize real-time mapping of reservoir performance. Introduce the Proximal Policy Optimization (PPO) algorithm to autonomously learn the optimal construction strategy in a twin environment, dynamically adjust fracturing parameters, hole filling positions, and plugging timing. In order to further improve the transparency and adaptability of decision-making, attention mechanism is embedded to realize the visualization of multi-source feature weights, and Bayesian reasoning is combined to update the uncertainty of formation parameters online. The experimental results show that compared with the traditional staged optimization and static multi-objective methods, the cumulative oil production, operating cost and risk event control are increased by 21.6%, decreased by 3.5% and controlled below 2% respectively, and the comprehensive benefit index reaches 0.89, which is significantly better than the existing schemes. The research results provide an effective technical path for online optimization and interpretable decision-making of stimulation measures in intelligent oil fields.
Hongzhu Zhao (Sun,) studied this question.