A virtual flow meter (VFM) enables continuous prediction of flow rates in petroleum production systems. The predicted flow rates may aid the daily control and optimization of a petroleum asset. Gray-box modeling is an approach that combines mechanistic and data-driven modeling. The objective is to create a computationally feasible VFM for use in real-time applications, with high prediction accuracy and scientifically consistent behavior. This article investigates five different gray-box model types in an industrial case study using real, historical production data from 10 petroleum wells, spanning at most four years of production. The results are diverse with an oil flow rate prediction error in the range of 1.8%-40.6%. Further, the study casts light upon the nontrivial task of balancing learning from both physics and data. Consequently, providing general recommendations towards the suitability of different hybrid models is challenging. Nevertheless, the results are promising and indicate that gray-box VFMs may reduce the prediction error of a mechanistic VFM while remaining scientifically consistent. The findings motivate further experimentation with gray-box VFM models and suggest several future research directions to improve upon the performance and scientific consistency.
翻译:虚拟流量计( VFM) 能够持续预测石油生产系统的流量。 预测流量率可能有助于石油资产的日常控制和优化。 Gray-box模型是一种将机械和数据驱动模型相结合的方法。 目标是创建一种可计算可行的VFM模型,用于实时应用,具有高预测准确性和科学一致性的行为。 文章利用10口石油油井的真实历史生产数据,对工业案例研究中的5种不同的灰箱模型进行了调查,该模型覆盖了最长4年的生产。 结果各不相同,石油流量预测误差在1.8-40.6%之间。 此外,该研究还展示了平衡物理和数据学习的非边际任务。 因此,为不同混合模型的适合性提出一般建议具有挑战性。 然而,结果很有希望,并表明灰箱VFM模型在科学上可以减少机械化VFM模型的预测误差。 研究结果鼓励用灰箱VFM模型进行进一步实验,并提出未来改进性能和科学一致性的若干研究方向。