Gas injection (GI) pressure-volume-temperature (PVT) laboratory data play an important role in evaluating the performance and efficiency of enhanced oil recovery (EOR) processes, including carbon dioxide (CO2) injection. Although typically there is a large conventional PVT laboratory data set corresponding to hydrocarbon reservoirs, GI laboratory studies are relatively scarce. Performing EOR laboratory studies may be either unnecessary in the case of EOR screening, or unfeasible in the case when reservoir fluid composition at current conditions is different from initial conditions. Given that GI is to be widely evaluated as a potential EOR process and the critical emerging importance of CO2 storage, there is increased demand for time- and cost-effective solutions to predict the outcome of associated GI laboratory experiments.

中文翻译：

---

基于物理增强机器学习的注气流体特性预测——专注于二氧化碳注入

注气 (GI) 压力-体积-温度 (PVT) 实验室数据在评估提高采收率 (EOR) 工艺（包括二氧化碳 (CO2) 注入）的性能和效率方面发挥着重要作用。尽管通常存在与碳氢化合物储层相对应的大型常规 PVT 实验室数据集，但 GI 实验室研究相对较少。在 EOR 筛选的情况下进行 EOR 实验室研究可能是不必要的，或者在当前条件下的储层流体成分与初始条件不同的情况下进行 EOR 实验室研究可能是不可行的。鉴于 GI 将作为一种潜在的 EOR 过程进行广泛评估，并且二氧化碳封存的重要性日益凸显，因此对时间和成本效益高的解决方案的需求不断增加，以预测相关 GI 实验室实验的结果。