卷积神经网络
计算机科学
人工智能
人工神经网络
口译(哲学)
反演(地质)
可靠性(半导体)
试验数据
深度学习
机器学习
模式识别(心理学)
算法
地质学
古生物学
物理
构造盆地
功率(物理)
程序设计语言
量子力学
作者
Peng Dong,Zhiming Chen,Xinwei Liao,Wei Yu
标识
DOI:10.1016/j.petrol.2021.109264
摘要
Pressure transient well test analysis is an important tool for identifying reservoir characteristics. However, the reliability of the results from well test analysis could be uncertain due to the analysts' lack of experience. This study aims to apply one-dimensional convolutional neural networks (1D CNN) and build an automatic interpretation model of well test data. The model can automatically identify not only the curve type but also the associated parameters. We integrate this automatic interpretation model with four classic well test models, with no model architecture adjustment and hyper-parameters. We validate the results that the curve classification accuracy reaches 97 %, and the median relative error of the curve parameter inversion is approximate 10 %. In addition, the performance of 1D CNN is compared to the artificial neural network (ANN) and two-dimensional convolutional neural networks (2D CNN). Results show that the 1D CNN has a faster training speed and has better accuracy in parameter inversion than ANN and 2D CNN. Finally, the automatic interpretation model is further validated with three field cases.
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