基于集成迁移学习的机械钻速预测

在钻井过程中, 钻速是指机械钻头破岩加深钻口的速度, 是反映钻井效率的一个重要指标. 近年来机器学习方法被应用于机械钻速预测, 然而实践中发现这些方法应用于新油田时, 预测精度显著下降, 主要原因是新油田可供学习训练的数据通常很少甚至完全缺失. 因此提升针对新油田的机械钻速预测性能是一个有待解决的问题. 针对该问题, 本文提出了一种基于迁移学习的跨油田机械钻速预测方法, 构建了一种带物理约束的集成迁移回归模型预测新油田的机械钻速. 在真实钻井数据集上的实验表明, 本文提出的机械钻速预测方法是有效的, 预测精度也显著优于目前主流的同类方法.

Rate of Penetration Prediction Using Ensemble Transfer Learning

In the process of drilling, the speed at which a drill bit breaks through rock and deepens the drill hole is called the rate of penetration (ROP), which is an important index reflecting drilling efficiency. In recent years, machine learning methods have been applied to the ROP prediction. However, it is found in practice that the prediction accuracy of ROP based on existing machine learning methods is significantly reduced when applied to new oil fields, and the main reason is that the data available for learning and training in these new fields are usually scarce or even completely missing. Therefore, improving the prediction performance of ROP in new oil fields is an important issue to be solved. Considering this, a cross-oilfield ROP prediction method based on transfer learning is proposed, and a boosting transfer regression model with physical constraints is constructed to predict ROP of new oil fields. The experiments on real drilling datasets indicate that the proposed method is effective, and the prediction accuracy is significantly better than that of the current mainstream ROP prediction methods.