利用机器学习消除VIV识别中的误报

P. Agarwal, K. Bhalla, R. Campbell
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引用次数: 0

摘要

在中高电流速度钻井时,涡激振动(VIV)是一个重要的考虑因素。钻井作业计划通常包括利用模型模拟确定最大钻井隔水管运动幅度的限制。然后,通过比较使用一个或多个运动传感器在现场测量的运动,这些限制可用于在现场发出警报。这种报警极限的确定具有挑战性,因为涡激振动是一个高度非线性的过程,电流剖面的速度或形状的微小变化可能导致钻井隔水管的涡激振动疲劳结果大不相同,特别是在深水中。我们使用前馈神经网络这一强大的机器学习算法,开发了一种区分破坏性和非破坏性VIV事件的分类器。该神经网络仅使用来自三个运动传感器的加速度和角速率数据,这些传感器分别位于上部弯曲关节、下部弯曲关节和防喷器组。为了训练神经网络,SHEAR7在钻井隔水管模型上运行生成隔水管运动和疲劳损伤数据。为了从实际现场环境中捕捉VIV响应的全部变化,从深水地点(水深> 6000英尺)的当前系泊处测量的数千个电流剖面被用作SHEAR7模型的输入。结果表明,神经网络分类器对损伤和非损伤VIV的预测几乎都是正确的。神经网络分类器的准确率、召回率和F1分数(准确率和召回率的组合)都接近100%。分类器的高精度、召回率和F1分数意味着它没有假阳性和假阴性。在这里,假阴性被定义为发生破坏性VIV,但被识别为非破坏性VIV事件且未发出警报的情况。假阳性是指当事件实际上并没有那么具有破坏性时,对破坏性VIV发出警报的情况。另一方面,对于上下弯曲关节角度保守选择限制的基线“常数”分类器(来自相同的数据)导致非常低的精度和F1分数,这意味着有太多的误报。虽然基线分类器不能预测任何假阴性,但由于有太多假阳性,它的成本非常高。此外,它还存在因误报过多而被用户忽略的风险。这项工作表明,机器学习技术可以使用最少数量的传感器准确预测现场的破坏性VIV事件。使用传统方法是不可能做出如此准确的预测的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Elimination of False Positives in VIV Identification Using Machine Learning
Vortex-induced-vibration (VIV) is an important consideration while drilling at sites with moderate to high current speeds. Planning for drilling operations often includes determination of limits on maximum drilling riser motion amplitude using model simulations. These limits can then be used to raise alarms in the field by comparing motions measured in the field using one or more motion sensors. The determination of such alarm limits is challenging as VIV is a highly nonlinear process, and small changes in the speed or shape of current profile can result in quite different VIV fatigue results for drilling risers, especially in deep water depths. We use feed-forward neural network, which is a powerful machine learning algorithm, to develop a classifier for distinguishing damaging and non-damaging VIV events. The neural network uses acceleration and angular rate data from only three motions sensors located on the upper flex joint, the lower flex joint and the BOP stack. To train the neural network, riser motions and fatigue damage data are generated from SHEAR7 runs on the model of the drilling riser. Thousands of current profiles measured from a current mooring at a deepwater site (water depth > 6,000 ft) are used as inputs to SHEAR7 model in order to capture full variability in VIV response from the actual field environment. Results show that the neural network classifier almost always predicts damaging and non-damaging VIV correctly. The precision, recall, and F1 score (a combination of precision and recall) for the neural network classifier are all close to 100%. A high precision, recall, and F1 score for a classifier implies that it has no false positives and no false negatives. Here, a false negative is defined as the situation when damaging VIV occurred but was identified as a non-damaging VIV event and an alarm is not raised. False positive is the situation when an alarm is raised for damaging VIV when the event was actually not so damaging. On the other hand, the baseline "constant" classifier of conservatively chosen limits (from the same data) for upper and lower flex joint angles results in very low precision and F1 scores, implying too many false positives. While the baseline classifier does not predict any false negatives, it is very expensive because of too many false positives. Furthermore, it carries the risk of being ignored by users due to too many false alarms. This work demonstrates that machine learning techniques can accurately predict damaging VIV events in the field using minimal number of sensors. Such accurate predictions were not possible using traditional methods.
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