Research onconvolutional neural network for reservoir parameter prediction

You-xiang DUAN; Gen-tian LI; Qi-feng SUN

doi:10.11959/j.issn.1000-436x.2016240

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Research onconvolutional neural network for reservoir parameter prediction

Contents Papers | 更新时间：2024-06-05

- Research onconvolutional neural network for reservoir parameter prediction
- Journal on Communications Vol. 37, Issue Z1, Pages: 1-9(2016)
- 作者机构：
  
  中国石油大学（华东）计算机与通信工程学院，山东青岛 266580
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2016240
  CLC： TP391
- Online First：2016-10，
  
  Published：25 October 2016
- 稿件说明：
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You-xiang DUAN, Gen-tian LI, Qi-feng SUN. Research onconvolutional neural network for reservoir parameter prediction[J]. Journal on Communications, 2016, 37(Z1): 1-9.
DOI：

You-xiang DUAN, Gen-tian LI, Qi-feng SUN. Research onconvolutional neural network for reservoir parameter prediction[J]. Journal on Communications, 2016, 37(Z1): 1-9. DOI： 10.11959/j.issn.1000-436x.2016240.

摘要

人工神经网络作为人工智能的分支，在模式识别、分类预测等方面已成功地解决了许多现代计算机难以解决的实际问题。然而随着人工智能的发展，神经网络的自主性特征学习功能越来越重要，人工神经网络虽然表现出了良好的智能特性，但不能自主地学习特征。近年来，深度学习逐渐崛起，围绕深度神经网络的研究也越来越多，但其在地质储层参数预测领域的研究还很少。提出了一种应用卷积神经网络对地质储层参数进行预测的方法，该方法不仅能对储层参数进行精确预测，而且可以得到储层特征集。实验证明，卷积神经网络可以应用于地质储层参数预测，且预测精度较高，同时卷积神经网络的卷积特征为储层地质建模与测井资料解释提供了重要的支持。

Abstract

As the branch of artificial intelligence

artificial neural network solved many difficult practical problems in pattern recognition and classification prediction field successfully.However

they cannot learn the feature from networks.In recent years

deep learning becomes more and more advanced

but the research on the field of geological reservoir pa-rameter prediction is still rare.A method to predict reservoir parameters by convolutional neural network was presented

which can not only predict reservoir parameters accurately

but also get features of the geological reservoir.The study es-tablished the convolutional neural network model.Results show that the convolutional neural network can be used for reservoir parameter prediction

and get high prediction precision.Moreover

convolutional features from convolutional neural network provided important support for geological modeling and logging interpretation.

关键词

Keywords

references

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