Abstract:

Predicting the rate of penetration (ROP) plays a significant role in optimizing drilling parameters, improving drilling efficiency, and reducing costs. Although intelligent algorithms have achieved promising results in ROP prediction, existing methods generally ignore the impact of drill bit wear on ROP. To address this technical bottleneck, this study proposes a ROP prediction model incorporating drill bit wear, which establishes a dual neural network architecture for predicting drill bit wear coefficients and ideal ROP. This architecture enables modeling of the complex nonlinear relationships among drilling parameters, wear states, and ROP. Aiming at the scarcity of real-time drill bit wear labels, a pretraining mechanism is proposed to obtain wear coefficients through a two-step training process. Comparative experiments based on measured data from Blocks A and B in Bohai oilfield show that: (1) The prediction accuracy of the porposed model for ROP is improved by 100% and 27% in Blocks A and B, respectively, compared with traditional machine learning methods, and by 14% and 7.6% compared with BP neural network models, significantly surpassing the performance of traditional data-driven models. (2) The proposed model demonstrates improvements in prediction performance in the shallow strata with complex lithology (Block A) than in the deep strata with stable lithology (Block B). (3) The proposed pretraining mechanism enables the model to predict drill bit wear coefficients without real-time wear labels and simultaneously improves the prediction accuracy of mechanical ROP by 24% and 10% in the two blocks, respectively. The coupled model and pretraining mechanism developed in this study not only provide a more accurate method for mechanical ROP prediction but also offer an effective means for real-time monitoring of drill bit wear states, providing practical guidance for drilling operations.

Key words: bit wear, rate of penetration prediction, deep learning, pretraining

摘要：

机械钻速的预测对于优化钻井参数、提升钻井效率和节约钻井成本具有重要意义。尽管智能算法在机械钻速预测中取得了较好的效果，但现有方法普遍忽视钻头磨损状态对机械钻速的影响。针对这一技术瓶颈，本文提出了一种耦合钻头磨损的机械钻速预测模型，通过构建钻头磨损系数预测与理想钻速预测的双层神经网络架构，实现了钻井参数、磨损状态与钻速之间复杂非线性关系的建模。针对钻头实时磨损标签稀缺的挑战，本文提出了一种预训练学习机制，分两步训练得到钻头磨损的系数。基于渤海油田A、B区块实测数据的对比实验表明：(1)提出模型的预测精度较传统机器学习方法在A、B区块分别提升100%和27%，较BP神经网络模型提升14%和7.6%，显著超越传统数据驱动模型的性能；(2)在岩性复杂的浅部地层(A区块)的预测效果提升优于岩性稳定的深部地层(B区块)；(3)提出的预训练学习机制能够使模型在无实时磨损标签条件下实现钻头磨损系数预测，并能同步提升机械钻速的预测精度，在两类区块分别提升24%和10%。本研究构建的耦合模型与预训练机制，既为机械钻速预测提供了更高精度的预测方法，也为钻头磨损状态的实时监测提供了有效的表征手段，能为钻井作业提供有效的指导。

关键词: 钻头磨损, 钻速预测, 深度学习, 预训练

CLC Number: 

• TE142

• TP18