Artificial Neural Network for Prediction of Hydrate Formation Temperature

Odutola Toyin Olabisi, Ajienka Joseph Atubokiki, O. Babawale
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引用次数: 3

Abstract

Gas hydrate deposition is one of the major Flow Assurance problems in petroleum production in the offshore environment. Therefore, is important to accurately predict hydrate formation conditions and avoid these conditions or propose a hydrate management plan. This study compares the effectiveness of Artificial Neural Network (ANN) for predicting hydrate formation temperature to the effectiveness of other hydrate temperature prediction correlations such as: Towler and Mokhtab correlation, Hammerschmidt correlation and Bahadori and Vuthalaru correlation. The ANN was trained using 459 hydrate formation experimental data points from Katz chart and Wilcox et al chart. Pressure (P) and specific gravity (ϒ) were chosen as the inputs in the 4-layer network while temperature was the output. The data points were for gases of specific gravity of 0.5539, 0.6, 0.7, 0.8, 0.9 and 1.0. The experimental pressures considered were from 49 psia to 4000 psia. The Neural Network was built using an excel add-in tool, NEUROXL. ANN accurately predicted the experimental hydrate formation temperature with the regression coefficient greater than 0.98 for the different specific gravities considered. Moreso, the error analysis shows ANN performed better than Towler and Mokhtab correlation, Hammerschmidt correlation and Bahadori and Vuthalaru correlation because it had the least Mean Absolute percentage error, MAPE (3.5) compared to the other correlations. ANN is a viable tool for hydrate prediction and the current model can be improved upon by including more experimental data in the ANN.
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水合物形成温度预测的人工神经网络
天然气水合物沉积是海上油气生产中主要的流动保障问题之一。因此,准确预测水合物形成条件、避免水合物形成条件或提出水合物管理方案具有重要意义。本研究将人工神经网络(ANN)预测水合物形成温度的有效性与其他水合物温度预测相关性(如:Towler and Mokhtab相关性、Hammerschmidt相关性和Bahadori and Vuthalaru相关性)的有效性进行了比较。人工神经网络使用Katz图和Wilcox等人图中的459个水合物形成实验数据点进行训练。压力(P)和比重(y)被选为4层网络的输入,温度是输出。数据点适用于比重为0.5539、0.6、0.7、0.8、0.9和1.0的气体。实验压力范围为49psia至4000psia。神经网络是使用excel插件NEUROXL构建的。在考虑不同比重的情况下,人工神经网络准确预测了实验水合物形成温度,回归系数大于0.98。此外,误差分析表明,ANN比Towler和Mokhtab相关、Hammerschmidt相关和Bahadori和Vuthalaru相关表现更好,因为与其他相关相比,它具有最小的平均绝对百分比误差MAPE(3.5)。人工神经网络是一种可行的水合物预测工具,目前的模型可以通过在人工神经网络中加入更多的实验数据来改进。
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