Towards better shale gas production forecasting using transfer learning
IF 2.6
Q3 ENERGY & FUELS
引用次数: 10
Abstract
Deep neural networks can generate more accurate shale gas production forecasts in counties with a limited number of sample wells by utilizing transfer learning. This paper provides a way of transferring the knowledge gained from other deep neural network models trained on adjacent counties into the county of interest. The paper uses data from more than 6000 shale gas wells across 17 counties from Texas Barnett and Pennsylvania Marcellus shale formations to test the capabilities of transfer learning. The results reduce the forecasting error between 11% and 47% compared to the widely used Arps decline curve model.
利用迁移学习进行页岩气产量预测
摘要深度神经网络利用迁移学习技术，可以在样本井数量有限的县域条件下，获得更准确的页岩气产量预测结果。本文提供了一种将从相邻县训练的其他深度神经网络模型中获得的知识转移到感兴趣县的方法。本文使用了来自德克萨斯州Barnett和宾夕法尼亚州Marcellus页岩地层17个县的6000多口页岩气井的数据来测试迁移学习的能力。与广泛使用的Arps下降曲线模型相比，该模型的预测误差降低了11% ~ 47%。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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