基于石油钻井机械钻速预测的迁移学习理论研究

Research on Transfer Learning Theory Based on Prediction of Penetration Rate of Oil Drilling Machinery

摘要: 提高钻速预测模型的迁移性有助于实现对钻井机械钻速高效、精准的预测。经过深度调研，总结了迁移学习与传统机器学习的关系，迁移学习是机器学习范畴内一个重要的研究领域；详细对比了迁移学习模式与传统机器学习模式的差异性，并介绍了各类迁移学习方法的特点。深入研究了基于实例的迁移学习方法，对基于迁移学习理论的机械钻速预测模型进行了可行性分析并完成模型设计。

Abstract: Improving the mobility of drilling rate prediction models can help achieve efficient and accurate prediction of drilling machinery drilling rate. After in-depth investigation, the relationship between transfer learning and traditional machine learning was summarized. Transfer learning is an important research field in the field of machine learning. The differences between transfer learning models and traditional machine learning models were compared in detail, and various types of transfer learning characteristics of the method were introduced. The case-based transfer learning method was studied in depth, the feasibility analysis of the mechanical penetration rate prediction model based on the transfer learning theory was conducted, and the model design was completed.