论文标题
开发混合数据驱动的机械性虚拟流量计 - 案例研究
Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter -- a Case Study
论文作者
论文摘要
虚拟流量计，预测石油资产生产流量的数学模型是生产监测和优化的有用辅助工具。基于第一原理的机械模型最常见，但是，在测量中利用模式的数据驱动模型正在越来越受欢迎。这项研究调查了一种混合建模方法，利用了上述专业知识领域的技术来对井生产障碍进行建模。扼流圈用简化的第一原理方程和神经网络表示，以估计阀流量系数。来自石油平台Edvard Grieg的历史生产数据用于模型验证。此外，构建了机械和数据驱动模型以比较性能。建立了具有不同程度的混合性和随机优化参数的模型开发的实用框架。混合模型性能的结果很有希望，尽管有相当大的改进空间。
Virtual flow meters, mathematical models predicting production flow rates in petroleum assets, are useful aids in production monitoring and optimization. Mechanistic models based on first-principles are most common, however, data-driven models exploiting patterns in measurements are gaining popularity. This research investigates a hybrid modeling approach, utilizing techniques from both the aforementioned areas of expertise, to model a well production choke. The choke is represented with a simplified set of first-principle equations and a neural network to estimate the valve flow coefficient. Historical production data from the petroleum platform Edvard Grieg is used for model validation. Additionally, a mechanistic and a data-driven model are constructed for comparison of performance. A practical framework for development of models with varying degree of hybridity and stochastic optimization of its parameters is established. Results of the hybrid model performance are promising albeit with considerable room for improvements.
