基于改进即时学习算法的动液面软测量建模

油田动液面参数软测量预测应用中,软测量模型随生产的进行会逐步退化,导致预测结果偏差较大,无法在油田生产过程中加以使用.对此,提出采用基于子空间相似度的即时学习策略来对动液面预测模型进行自适应动态更新.通过对生产阶段数据进行子空间的相似度计算,提高建模样本选取的准确性.设计两个记忆参数改变以往即时学习策略模型的更新方法,在减少计算量的同时提高动液面的预测精度.与以往即时学习算法进行实验对比,结果表明,改进算法对油田动液面测量精度高,适应性强,符合油田生产标准,可以应用于油田实际生产.

Soft Sensor for Determination of Dynamic Fluid Levels Based on Enhanced Just-in-Time Learning Algorithm

When soft sensor model is used to predict dynamic fluid levels in oil production， it will gradually degenerate during the process， resulting in the larger deviation of prediction results and the difficulties to be used in practical oilfield production. To solve this problem， a new just-in-time model based on the similarity of subspaces was proposed to realize adaptive dynamic updates for a prediction model of dynamic fluid level. According to the production data， the similarity of subspaces was calculated to improve the accuracy of selecting modeling samples. Two memory parameters were designed to change the update method in traditional just-in-time learning model， which could reduce the amount of calculation and improve the prediction accuracy of dynamic fluid level. Compared with the traditional just-in-time learning algorithm， the improved method has better measurement accuracy and adaptation for the prediction of dynamic fluid levels. The example showed that the proposed method was fitted in with the standard of oil production and could be applied to actual production.