卷积神经网络
口译(哲学)
试井(油气)
计算机科学
井筒
电流(流体)
算法
人工神经网络
领域(数学)
试验数据
储层建模
数据挖掘
人工智能
石油工程
数学
工程类
电气工程
程序设计语言
纯数学
作者
Xuliang Liu,Wenshu Zha,Daolun Li,Xiang Li,Luhang Shen
出处
期刊:Journal of Energy Resources Technology-transactions of The Asme
[ASME International]
日期:2022-09-14
卷期号:145 (3)
被引量:1
摘要
Abstract In order to develop reservoirs rationally, accurate reservoir parameters are usually obtained through well test analysis. However, a good deal of well test data with changing wellbore storage characteristics bring difficulties to the current well test interpretation, so it is important to find a valid interpretation method for changing well storage reserves data. This paper proposed an automatic well test interpretation method based on one-dimensional convolutional neural network (1D CNN) for circular reservoir with changing wellbore storage. Compared with two-dimensional convolutional neural network (2D CNN), 1D CNN significantly reduces the computational complexity and time cost. The CNN takes pressure change and pressure derivative data of the log–log plot as input and reservoir parameters as output of network. This method applies two 1D CNNs respectively to fit two types of reservoir parameters, one type includes CDe2s, CαD, and CϕD and the other type is boundary distance R. In addition, the training samples of the two networks are different according to different parameters. The two-network approach reduces the difficulty of extracting curve characteristics and improves interpretation ability. The effectiveness of this method is proved by the field data in Daqing oilfield. The method greatly improves the working efficiency of well test interpreters and can be widely used.
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