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Data-driven soft sensor of downhole pressure for a gas-lift oil well

机译：数据驱动的气举油井井下压力软传感器

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Downhole pressure is a key variable in the operation of gas-lift oil wells. However, maintaining and replacing downhole sensors is a challenging task. In this context, we design and implement a data-driven soft sensor to estimate online the downhole pressure based on other (seabed and platform) available measurements. Such application is based on a two-step procedure. In the first step, discrete-time black-box and gray-box NARX models are identified offline and independently using historical data. Both polynomial and neural models are obtained. In the second step, recursive predictions of these multiple models are combined with current measured data (of variables other than the downhole pressure) by means of an interacting bank of unscented Kalman filters. In doing so, a closed-loop model prediction is performed. Three issues are investigated in this paper concerning: (ⅰ) the usage of a filter bank rather than a single filter approach, (ⅱ) the availability of seabed variables as inputs of the models compared to the case where only platform variables are available, and (ⅲ) the employment of gray-box models in the filters. Experimental results along 7 months of tests indicate that such closed-loop scheme improves estimation accuracy and robustness compared to the free-run model prediction or to the use of a single unscented Kalman filter. The method employed in this paper can also be applied to other soft sensing applications in industry.
机译：井下压力是气举油井作业中的关键变量。但是，维护和更换井下传感器是一项艰巨的任务。在这种情况下，我们设计并实现了一个数据驱动的软传感器，以基于其他（海床和平台）可用的测量值在线估算井下压力。此类应用程序基于两步过程。第一步，离线和独立使用历史数据识别离散时间的黑盒和灰盒NARX模型。获得多项式和神经模型。在第二步中，通过无味卡尔曼滤波器的交互作用，将这些多个模型的递归预测与当前测量数据（除井下压力以外的变量）组合在一起。这样做，执行闭环模型预测。本文研究了以下三个问题：（ⅰ）使用过滤器库而不是使用单个过滤器方法；（ⅱ）与仅使用平台变量的情况相比，海床变量作为模型输入的可用性；以及（ⅲ）在筛选器中使用灰盒模型。经过7个月的测试，实验结果表明，与自由运行模型预测或使用单个无味卡尔曼滤波器相比，这种闭环方案可提高估计准确性和鲁棒性。本文采用的方法也可以应用于工业中的其他软传感应用。

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