The CO₂ flooding with superior displacement efficiency and high injectivity is an efficient enhanced oil recovery method. However, due to the unfavorable sweep efficiency particularly for strong heterogeneous reservoirs and immiscible flooding, the oil recovery on site is not all favorable. Multi-well rates optimization is one of common measures improving sweep efficiency with easy implement and low cost There are many rates optimization methods have been proposed by now. In this research, we first introduced the multi-agent deep deterministic policy gradient (MADDPG) algorithm to the multi-well rates optimization of CO₂ flooding, and the new rates optimization method was built. The MADDPG adopts the centralized training and decentralized execution algorithm framework, and overcomes the defect that the single-agent reinforcement learning cannot deal the multi-well rates optimization well and also avoids the dimensional disaster problems. We treated each well as an agent, and each agent has its own reward, state and action. We chose the net present value (NPV) as the reward, the injection-production rate change range as the action element, and the production time, the bottom hole pressure, the oil production rate, and the gas-oil ratio as the state elements. The simulation results show that the optimal case obviously improves the NPV compared with the base case, and the simulation case with strong heterogeneity and immiscible flooding can also converge to the optimal target, which prove the effectiveness and robustness of the rates optimization method respectively. This research provides recommendations that improving the oil recovery by increasing the sweep efficiency to increase the income, and reducing the invalid CO₂ injection to decrease the cost can achieve the optimal NPV. Reservoir heterogeneity seriously impairs the rates optimization performance, and rates optimization makes little difference to extreme strong interlayer heterogeneity for serious interlayer influence.

CO ₂驱油具有优越的驱替效率和高注入能力，是一种高效的提高采收率的方法。然而，由于波及效率不佳，特别是对于强非均质油藏和非混相驱，现场采收率并非都是有利的。多井速率优化是提高波及效率的常用措施之一，实施简单，成本低。目前已经提出了多种速率优化方法。在这项研究中，我们首先将多智能体深度确定性策略梯度（MADDPG）算法引入到CO _{2的多井率优化中。}洪水，并建立了新的费率优化方法。MADDPG采用集中训练和分散执行的算法框架，克服了单智能体强化学习不能很好地处理多井率优化的缺陷，也避免了维度灾难问题。我们把每一口井都当作一个代理，每个代理都有自己的奖励、状态和行动。以净现值（NPV）为奖励，注采速度变化幅度为作用要素，采油时间、井底压力、产油量、气油比为状态要素. 仿真结果表明，与基准情况相比，最优情况明显提高了 NPV，强非均质性和非混相驱的模拟案例也能收敛到最优目标，分别证明了速率优化方法的有效性和鲁棒性。本研究提出了通过提高波及效率来增加收益、减少无效CO来提高采收率的建议。_2次注入降低成本可以达到最优NPV。储层非均质性严重影响了速率优化性能，而速率优化对极强的层间非均质性影响不大，层间影响严重。