替代模型
计算机科学
卷积神经网络
人工智能
卷积(计算机科学)
数据同化
深度学习
匹配(统计)
人工神经网络
数据挖掘
模式识别(心理学)
机器学习
算法
数学
统计
物理
气象学
作者
Xiaopeng Ma,Kai Zhang,Jian Wang,Chuanjin Yao,Yongfei Yang,Hai Sun,Jun Ye
出处
期刊:Spe Journal
[Society of Petroleum Engineers]
日期:2021-12-06
卷期号:27 (02): 1160-1175
被引量:11
摘要
Summary Surrogate modeling has shown to be effective in improving the solving efficiency for history matching in the development of oil and gas, but the traditional surrogate models are difficult to directly deal with the high-dimensional spatial uncertain parameters, such as the permeability field. In this paper, we introduce a new deep-learning-based surrogate modeling framework, image-to-sequence regression, which can directly predict the production data from the high-dimensional spatial parameters. Specifically, a spatial-temporal convolution recurrent neural network surrogate model is proposed based on a densely connected convolutional neural network (CNN) model and a stacked multilayer long short-term memory (LSTM) model. And a surrogate-based history-matching workflow is then developed by combining the proposed surrogate model with an improved ensemble smoother data assimilation algorithm. Two case studies on a 2D and a 3D reservoir model demonstrate that the proposed surrogate model can effectively predict production data and improve the efficiency of history matching.
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