数字时代的钻井:利用机器学习优化机械钻速的方法

P. Batruny, H. Yahya, N. Kadir, A. Omar, Z. Zakaria, Saravanan Batamale, Noreffendy Jayah
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引用次数: 4

摘要

在一个已知岩性的地区,机械钻速(ROP)很低。该地区丰富的钻井经验和相似的钻井条件为作业者提供了足够的数据,通过使用机器学习来改善钻井计划和成本效益。机器学习,特别是人工神经网络(ANN),是一种发现多个输入之间关系的统计工具。数学模型可能会遗漏或认为是异常值的细节可以在人工神经网络模型中解释和解释。人工神经网络是在特定深度区间选定井记录的数千个实时数据点上进行训练的。典型的钻井参数,如钻头重量、转速、钻头水力学、岩性和狗腿严重程度,是模型中选择的输入参数,以产生ROP。一旦模型被校准到历史数据,它就被用来寻找最佳参数来最大化ROP。12.25英寸的R平方因子分别为0.729和0.675。17.5英寸。章节安排。这是通过2个隐藏层的ANN结构实现的,每个隐藏层由5个节点组成。灵敏度分析确定了影响ROP的主要参数是钻头液压、钻头压重和转速。ROP模型用于进行“虚拟钻脱测试”,以确定最大ROP的钻井参数。ROP依赖于钻头重量和岩性分析,这表明钻头设计可以进一步改进。钻头水力学表明,在过平衡较大的井段,需要更高的流量。在四口井中测试了最佳钻井参数,与原始现场数据相比,ROP提高了50%以上。在一个日益由大数据主导的行业,将干净的数据与“噪音”区分开来将是一个至关重要的话题。本文旨在为使用机器学习以简单且易于复制的方式优化ROP提供蓝图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drilling in the Digital Age: An Aproach to Optimizing ROP Using Machine Learning
Low rates of penetration (ROP) were experienced in an area with well-known lithology. The vast drilling experience and similarity of drilling conditions in the area, provided the operator with enough data to improve the well schedule and cost performance through the use of machine learning. Machine learning, specifically artificial neural networks (ANN), is a statistical tool to find relations between multiple inputs. Details that would have been missed or considered outliers by a mathematical model can be accounted for and explained in the ANN model. The ANN was trained on thousands of real time data points recorded from selected wells in a specific depth interval. Typical drilling parameters such as weight on bit, rotary speed, bit hydraulics, lithological properties, and dogleg severity were the input parameters chosen in the model to generate ROP. Once the model was calibrated to historical data, it was used to find the best parameters to maximize ROP. R squared factors were 0.729 and 0.675 for 12.25 in. and 17.5 in. sections repectively. This was achieved with an ANN structure of 2 hidden layers consisting of 5 nodes each. Sensitivity analysis identified bit hydraulics, weight on bit, and rotary speed as the major parameters impacting ROP. The ROP model was used to conduct a "virtual drill-off test" to identify drilling parameters that maximize ROP. ROP dependency on weight on bit and lithological analysis suggests bit design can be further improved. Bit hydraulics showed that higher flow rate was needed in sections with higher overbalance. Optimum drilling parameters were tested on four wells and resulted in more than 50% higher ROP compared to original field data. In an industry increasingly dominated by big data, separating the clean data from the "noise" will be a vital topic. This paper aims to provide a blueprint for the use machine learning to optimize ROP in a manner that is simple and easily replicated.
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