Case Study ROP Modeling Using Random Forest Regression and Gradient Boosting in the Hanover Region in Germany

Patrick Höhn, Felix Odebrett, C. Paz, J. Oppelt
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引用次数: 4

Abstract

Reduction of drilling costs in the oil and gas industry and the geothermal energy sector is the main driver for major investments in drilling optimization research. The best way to reduce drilling costs is to minimize the overall time needed for drilling a well. This can be accomplished by optimizing the non-productive time during an operation, and through increasing the rate of penetration (ROP) while actively drilling. ROP has already been modeled in the past using empirical correlations. However, nowadays, methods from data science can be applied to the large data sets obtained during drilling operations, both for real-time prediction of drilling performance and for analysis of historical data sets during the evaluation of previous drilling activities. In the current study, data from a geothermal well in the Hanover region in Lower Saxony (Germany) were used to train machine learning models using Random Forest™ regression and Gradient Boosting. Both techniques showed promising results for modeling ROP.
基于随机森林回归和梯度增强的德国汉诺威地区ROP建模案例研究
油气行业和地热能行业的钻井成本降低是钻井优化研究投资的主要驱动力。降低钻井成本的最佳方法是尽量减少钻井所需的总时间。这可以通过优化作业期间的非生产时间,以及在主动钻井时提高机械钻速来实现。ROP在过去已经使用经验相关性进行了建模。然而,如今,数据科学的方法可以应用于钻井作业期间获得的大型数据集,既可以实时预测钻井性能,也可以在评估以往钻井活动期间分析历史数据集。在目前的研究中,来自德国下萨克森州汉诺威地区的地热井的数据被用于使用随机森林™回归和梯度增强来训练机器学习模型。这两种技术在ROP建模方面都显示出很好的结果。
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