The combination of depressurization and thermal stimulation is one of the most promising techniques for producing gas from natural gas hydrate reservoirs. The energy utilization efficiency is the core factor that determines whether the technology can be better applied to engineering practice. In contrast to previous works, this paper proposes an AI-based composition model, which realizes the dynamic optimization of the energy utilization efficiency based on the learning and evaluation of different thermal stimulation policies. As shown by the optimization results, the proposed model achieved good performance in both the hydrate recovery target and the economic target coordination by adjusting the coefficients of the optimization control policy function. Based on a systematic assessment of the energy utilization efficiency of different thermal stimulation policies, the model increased the energy production-injection ratio by 4.5 times in Scenario I, and the CH₄ recovery efficiency was increased by 1.6 times in Scenario II. The learning results of the AI model also showed that the reactor scale hydrate recovery had a size effect, such that the later the thermal energy injection was, the higher the energy efficiency that could be obtained. Moreover, through the model learning, the quantitative relationship between each thermal stimulation policy and its developmentally changing energy utilization efficiency was established. The results showed that in the initial hydrate recovery period, thermal stimulation policies with a high injection temperature and low injection rate achieved greater energy utilization efficiency. As recovery progressed, gradually lowering the injection temperature and increasing the injection rate gave the policy a higher energy utilization efficiency. The proposed methodology provides a feasible approach for dynamic thermal stimulation parameter optimization and improves the hydrate recovery economy.

减压和热刺激相结合是从天然气水合物储层中生产天然气的最有前途的技术之一。能源利用效率是决定该技术能否更好地应用于工程实践的核心因素。与以往的工作相比，本文提出了一种基于人工智能的组合模型，该模型基于对不同热刺激策略的学习和评估，实现了能源利用效率的动态优化。优化结果表明，该模型通过调整优化控制策略函数的系数，在水合物采收率目标和经济目标协调方面均取得了良好的性能。₄在场景二中，回收效率提高了 1.6 倍。AI模型的学习结果还表明，反应器规模的水合物回收具有尺寸效应，即热能注入越晚，可获得的能量效率越高。此外，通过模型学习，建立了每个热刺激政策与其发展变化的能源利用效率之间的定量关系。结果表明，在水合物采收初期，高注入温度和低注入速度的热刺激策略实现了更高的能源利用效率。随着采收率的提高，逐渐降低注入温度和提高注入速度，使该政策具有更高的能源利用效率。