基于小样本数据深度学习的砂体厚度预测方法及应用

doi:10.7657/XJPG20230214

摘要/Abstract

摘要：

沾化凹陷渤南洼陷北部Y184井区沙四上亚段储集层为多期扇三角洲沉积，具有单砂体厚度小、横向变化快、砂泥岩互层等特征，无法定量预测，制约了该井区的高效开发。综合利用深度学习和地震属性预测方法，通过构建虚拟井，解决研究区深度学习训练样本不足的问题，从而挖掘出砂体厚度与地震属性之间的非线性关系，建立了利用地震属性预测砂体厚度的网络模型，该方法能够较为准确地预测砂体厚度及其横向展布特征，提高了预测精度，为致密砂岩储集层预测提供了新的思路和方法。

关键词: 地震属性, 砂体厚度预测, 横向展布, 小样本数据, 深度学习, 网络模型

Abstract:

In the upper Es₄ in Y184 well block，northern Bonan subsag of Zhanhua sag， multi-stage fan delta deposits are developed，and characterized by thin sand bodies individually，great variation laterally and sandstone interbedded with shale，making it very difficult to perform quantitative prediction，which restricts the efficient development of this well block. Through deep learning and seismic attribute prediction，virtual wells were constructed to solve the problem of insufficient training samples for deep learning in the study area. Thus，a nonlinear relationship between sand body thickness and seismic attributes was clarified，and a network model for predicting sand body thickness using seismic attributes was established. This method can accurately predict sand body thickness and lateral distribution with a significantly improved accuracy，and provides a new idea for the prediction of tight sandstone reservoirs.

Key words: seismic attribute, sand body thickness prediction, lateral distribution, small sample data, deep learning, network model

中图分类号:

TE122.24

陈雨茂, 赵虎, 杨宏伟, 魏国华, 罗平平. 基于小样本数据深度学习的砂体厚度预测方法及应用[J]. 新疆石油地质, 2023, 44(2): 231-237.

CHEN Yumao, ZHAO Hu, YANG Hongwei, WEI Guohua, LUO Pingping. A Sand Body Thickness Prediction Method Based on Deep Learning From Small Sample Data and Its Application[J]. Xinjiang Petroleum Geology, 2023, 44(2): 231-237.

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