提出了一种物理辅助深度学习模型，通过集成基于物理和数据驱动的预测模型的互补优势，促进非常规油藏的迁移学习。开发的模型使用深度学习架构，使用低维特征空间表示将地层属性映射到相应的生产响应。结果表明，当超出范围（看不见的）输入参数必须从数据中推断时，基于物理的模拟数据可以促进生产预测，并且将从源场学到的权重转移到目标场可以添加有价值的信息以增强生产效率。目标场的预测性能。
虽然模拟模型的局限性使其对于开发非常规油藏并不可靠，但近几十年来钻探的井数量不断增加，为致密油水平井提供了广泛的数据库。如此大的数据集的使用允许使用数据驱动的代理模型。
A physics-assisted deep-learning model is presented to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models. The developed model uses a deep-learning architecture to map formation properties to their corresponding production responses using a low-dimensional feature space representation. The results indicate that physics-based simulated data can facilitate production predictions when out-of-range (unseen) input parameters have to extrapolate from data and that transferring the weights learned from the source field to the target field can add valuable information to enhance the prediction performance of the target field.
While the limitations of simulation models make them unreliable for developing unconventional reservoirs, the increasing number of wells drilled in recent decades has provided an extensive database for tight oil horizontal wells. The use of such large data sets has permitted the use of data-driven proxy models.
