在整篇论文中，作者提出了一种工作流程，该工作流程结合了概率建模和在一组物理模型上训练的深度学习模型，以提高页岩和致密储层预测的可扩展性和可靠性。他们的方法应用于二叠纪盆地的模拟案例和许多油井。通过事后研究，这些模型已被证明可以生成真实而多样的生产曲线，捕捉非常规流动的物理特性，量化油井生产前景的不确定性，并有助于解释地下不确定性。
在非常规资产开发领域，可扩展预测是预测可靠性的关键组成部分。近年来，数据驱动的机器学习模型和工作流程已成为预测油井性能的有力工具，特别是在油井具有相似的储层特性、完井设计和操作条件的情况下。
一种被称为物理信息机器学习（PIML）的新方法已获得广泛关注。
In the complete paper, the authors present a workflow that combines probabilistic modeling and deep-learning models trained on an ensemble of physics models to improve scalability and reliability for shale and tight reservoir forecasting. Their approach is applied to synthetic cases and many wells in the Permian Basin. Through hindsight studies, these models have been demonstrated to generate realistic and diverse production curves, capture the physics of unconventional flow, quantify well-production-outlook uncertainty, and help interpretation of subsurface uncertainty.
In the realm of unconventional asset development, scalable forecasting is a key component in forecast reliability. In recent years, data-driven machine-learning models and workflows have emerged as potent tools for predicting well performance, particularly in scenarios where wells share similar reservoir properties, completion designs, and operational conditions.
A novel approach known as physics-informed machine learning (PIML) has gained prominence.
