基于区域多井数据优选与模型预训练的深部地质钻探过程钻速动态预测方法

Dynamic prediction method of rate of penetration （ROP） in deep geological drilling process based on regional multi-well data optimization and model pre-training

摘要:

深部地质钻探过程钻速精准预测有助于提升钻探效率、降低钻探成本，可为安全高效的深部地质钻探施工提供关键技术支撑。本文提出了一种基于区域多井数据优选与模型预训练的深部地质钻探过程钻速动态预测方法。首先，选取岩性识别软件、钻进过程智能监控云平台、地质云系统等作为数据源，在此基础上设计深部地质钻探数据仓库。其次，运用区域多井数据优选技术在数据仓库中选择与目标井较匹配的数据，并开展钻速模型预训练。最后，结合深部地质钻探过程实钻数据，引入小波滤波、超限学习机、增量学习等技术，实现钻速预测模型动态更新。实验对比结果验证了所提方法具有很强的钻速预测性能与泛化能力。

Abstract:

Accurate prediction of rate of penetration （ROP） in deep geological drilling process can help to improve drilling efficiency and reduce drilling costs， which can provide key technical support for safety and efficient deep geological drilling construction. In this paper， a dynamic prediction method of ROP in deep geological drilling process based on regional multi-well data optimization and model pre-training is proposed. First， the deep geological drilling data warehouse is designed by selecting lithology identification software， drilling process intelligent monitoring cloud platform， and geological cloud system as data sources. Secondly， the regional multi-well data optimization technique is used to select the matching data with the target well in the data warehouse， and the ROP model is pre-trained on this basis. Finally， the ROP prediction model is dynamically updated through combining the actual drilling data of deep geological drilling process， and introducing techniques such as the wavelet filtering， extreme learning machine， and incremental learning strategy. The experimental comparison results verify that the proposed method has strong ROP prediction performance and generalization capability.