Two new, non-intrusive reduced order frameworks for the faster modelling of gas reservoirs with time-varying production are presented and compared. The first method is an extension of a method using proper orthogonal decomposition (POD) in conjunction with radial basis functions (RBFs) that has previously been applied to predicting the performance of oil reservoirs undergoing a constant rate waterflood. The second method uses an autoencoder rather than RBFs to estimate the flow dynamics (pressure distributions) in hyperspace for unseen cases. Both frameworks are ‘trained’ using sample outputs from off-line, commercial reservoir simulations of a realistic heterogeneous gas reservoir with time-varying production controls typical of gas field operation. These controls include time-varying rate and switching between bottom hole pressure and rate control as well as cases where wells get shut-in.

Both POD-based models produce reasonable forecasts of the reservoir performance for new unseen/prediction cases and are between 0.22 and 300 times faster than conventional simulation, including the time spent performing training simulations with conventional simulation solutions. The POD-RBF models are more accurate and consistent with reference commercial simulation outputs than the POD-AE models. In addition, the POD-AE models required more trial and error to set up as the number of hidden layers needed, depends on the particular scenario being modelled. There is no ab initio way of predicting the best number of layers for a given type of scenario. This makes them less suitable for practical application by reservoir engineers. Overall the POD-RBF framework is the most robust and accurate of the two methods.

提出并比较了两个新的、非侵入式降阶框架，用于更快地对具有时变产量的气藏进行建模。第一种方法是使用适当正交分解 (POD) 与径向基函数 (RBF) 相结合的方法的扩展，该方法先前已应用于预测经历恒定速率注水的油藏的性能。第二种方法使用自动编码器而不是 RBF 来估计超空间中未见情况的流动动力学（压力分布）。这两个框架都使用来自离线商业油藏模拟的样本输出进行“训练”，该模拟具有典型的气田操作时变生产控制的实际非均质气藏的商业油藏模拟。

两种基于 POD 的模型都能对新的未见/预测情况下的油藏性能做出合理的预测，并且比传统模拟快 0.22 到 300 倍，包括使用传统模拟解决方案执行训练模拟所花费的时间。POD-RBF 模型比 POD-AE 模型更准确且与参考商业模拟输出一致。此外，POD-AE 模型需要更多的试验和错误来设置，因为所需的隐藏层数量取决于所建模的特定场景。对于给定类型的场景，没有从头开始预测最佳层数的方法。这使得它们不太适合油藏工程师的实际应用。总体而言，POD-RBF 框架是两种方法中最稳健和最准确的。