基于大数据及人工智能的钻速实时优化技术

Real-time ROP optimization technology based on big data and artificial intelligence

黄小龙 刘东涛 宋吉明 韩雪银 乔纯上

HUANG Xiaolong LIU Dongtao SONG Jiming HAN Xueyin QIAO Chunshang

中海油能源发展股份有限公司工程技术分公司

CNOOC EnerTech-Drilling & Production Co., Tianjin 300451, China

海上钻井作业日费高昂，因此如何利用大数据及人工智能技术提高钻速，从而缩短作业工期、降低作业成本，是重要的研究课题之一。首先通过收集钻井现场的大数据信息，将录井数据、测井数据、钻井液性能等参数输入神经网络计算得到初次预测钻速，然后由最优化算法计算出实时全局最优解，从而建立基于机器学习方法和最优化算法的钻速实时优化模型，最后将模型嵌入可视化系统进行现场作业指导，从而提高钻井速度。以南海PY油田A井为试验井，进行了实时钻进过程的应用。实践表明，应用该技术不仅可有效提高钻速，并且对油田数字化发展具有借鉴意义。

In view that the daily operation cost of offshore drilling is quite high, one of the important research subjects is how to make use of big data and artificial intelligence technologies to improve the rate of penetration (ROP), so as to shorten the operation cycle and reduce the operation cost. In this paper, the big data information at drilling sites was collected, and mud logging data, wireline logging data and drilling fluid property were put into the neural network to calculate the initial predicted ROP. Then, the real-time global optimal solution was calculated by means of the optimization algorithm, and the real-time ROP optimization model based on machine learning method and optimization algorithm was established. Finally, the model was embedded into the visual system to guide the field operation, so as to realize ROP improvement. This technology was applied to the real-time drilling process by taking Well A of South China Sea PY Oilfield as the test well. The practice shows that this technology can not only improve ROP effectively, but is of reference significance to the digital development of oil fields.

10.13639/j.odpt.2021.04.005