完整的论文提出了一种数据驱动的代理模型，可以有效预测页岩中复杂裂缝网络水平井的产量。代理模型采用多层门控循环单元 (GRU) 单元，与新开发的深度学习方法、注意力机制 (Att-GRU)、跳跃连接和交叉验证相结合，以处理时间序列分析 (TSA) ）多变量操作和物理参数的问题。结果表明，Att-GRU方法可以准确预测具有复杂裂缝网络的页岩气井在给定时间和可变底孔压力（BHP）下的产量，同时保持较高的计算效率。
作者写道，他们的主要兴趣是开发基于物理的数据驱动模型，以加速页岩储层中复杂裂缝网络的模拟。结果表明，深度学习模型可以通过多变量物理输入忠实地重现可变 BHP 条件的所有复杂性。
在全文的这一部分中，阐明了所涉及的物理背景和相应的控制方程。
The complete paper proposes a data-driven proxy model to effectively forecast the production of horizontal wells with complex fracture networks in shales. With a multilayer gated recurrent unit (GRU) cell, the proxy model is coupled with newly developed deep-learning methods, an attention mechanism (Att-GRU), skip connection, and cross-validation to deal with time-series-analysis (TSA) issues of multivariate operating and physical parameters. Results indicate that the Att-GRU method can forecast the production for shale gas wells with complex fracture networks accurately at a given time and with variable bottomhole pressure (BHP) while maintaining high calculation efficiency.
The authors write that their main interest is to develop a physics-based data-driven model to speed up the simulation of complex fracture networks in shale reservoirs. Results show that the deep-learning model can faithfully recapitulate the variable BHP condition in all of its intricacies with multivariate physical input.
In this section of the complete paper, the involved physical background and the corresponding governing equations are clarified.
