钻井钻速（ROP）受多种因素影响，既有可控因素，也有不可控因素，肉眼难以区分。因此，神经网络等机器学习 (ML) 模型在钻井行业获得了发展势头。以前的模型是基于现场或基于工具的，这会影响训练领域之外的准确性。整篇论文的作者旨在开发一种普遍适用的全局 ROP 模型，减少为每个应用程序重新开发模型所需的工作量。
作者已经确定需要一种可以实时推荐参数的 ROP 模型，理想情况下需要一个可以以低预测误差应用的通用 ROP 模型。
可通过测井工具实时获取地层属性；然而，将测井数据纳入全球 ROP 模型具有挑战性。
Drilling rate of penetration (ROP) is influenced by many factors, both controllable and uncontrollable, difficult to distinguish with the naked eye. Thus, machine-learning (ML) models such as neural networks have gained momentum in the drilling industry. Previous models were field-based or tool-based, which affected accuracy outside of the trained field. The authors of the complete paper aim to develop one generally applicable global ROP model, reducing the effort needed to redevelop models for every application.
The authors have identified a need for an ROP model that can recommend parameters in real time, which ideally requires a general ROP model that can be applied with low prediction errors.
Formation properties are available in real time from logging tools; however, incorporating logging data into a global ROP model is challenging.
