基于深度全连接神经网络的储层有效砂体厚度预测  

Thickness Prediction of Reservoir Effective Sand Body by Deep Fully Connected Neural Network

作　　者：贺婷 周宁 吴啸宇 He Ting;Zhou Ning;Wu Xiaoyu(Nuclear Geology Brigade of Jiangxi Geological Bureau,Yingtan 335000,Jiangxi,China;Geographic Information Engineering Brigade,Jiangxi Provincial Bureau of Geology,Nanchang 330001,China)

机构地区：[1]江西省地质局核地质大队,江西鹰潭335000 [2]江西省地质局地理信息工程大队,南昌330001

出　　处：《吉林大学学报（地球科学版）》2023年第4期1262-1274,共13页Journal of Jilin University：Earth Science Edition

基　　金：江西省地质勘查基金管理中心项目(20163006);江西省地质环境项目(20171090);东华理工大学研究生创新基金项目(DYCA13015)。

摘　　要：河道砂是重要的油气储集体之一,实现砂体厚度的定量预测是提高油气开发效率的关键。随着目标储层非匀质性的增强,地震属性与储层岩性、物性、孔隙流体之间的关系更趋复杂。如何在地质信息有限的情况下实现高效且智能的复杂储层定量预测是目前储层预测领域的热点和难点。为了实现对致密砂岩储层的高精度智能化预测,本文提出基于深度全连接神经网络的储层有效砂体厚度预测方法。该方法通过构建多层堆叠的全连接神经网络逐层优化针对储层有效砂体厚度预测的地震属性,并将优化后的属性直接映射为砂体厚度。首先针对模型数据分析了训练样本对全连接神经网络建模的影响,然后在小样本情况下分别对比了该网络的深、浅层形态在网络规模大于训练样本数目及网络规模小于训练样本数目时的表现差异,发现当训练样本为小样本时,深层网络表现优于浅层网络,前提是训练样本数目大于网络规模。最后,我们将深度全连接神经网络用于胜利油田某区实际数据的有效砂体厚度预测,应用效果显示该方法对致密砂岩储层中4 m左右的砂体实现了有效识别,体现了该端到端智能建模方法从地震属性中挖掘潜藏地质信息的能力,证实了其在储层定量预测中的有效性。Channel sand is one of the important oil and gas reservoirs.The quantitative prediction of sand body thickness is the key to improve the efficiency of oil and gas development.With the enhancement of non-homogeneity of target reservoir,the relationship between seismic attribute and reservoir lithology,physical property and pore fluid becomes more complicated.How to achieve efficient and intelligent quantitative prediction of complex reservoirs under the condition of limited geological information is currently a hot and difficult topic in the field of reservoir prediction.To achieve high accuracy and intelligent prediction of the tight sandstone reservoir,a reservoir effective sand thickness prediction method based on deep fully connected neural network is proposed in this paper.The method constructs a multilayer stacked fully connected neural network to optimize the seismic attributes predicted for the effective sand thickness of the reservoir layer by layer,and maps the optimized attributes directly to the sand thickness.We first analyze the influence of training samples on fully connected neural network modeling,and then compare the performance of the deep and shallow morphology of this network in the case of small samples when the model size is larger than the number of training samples and the model size is smaller than the number of training samples,and find that the deep network outperforms the shallow one when the training samples are small,provided that the number of training samples is larger than the model size.Finally,we apply the deep fully connected neural network to the effective sand body thickness prediction from real data of Shengli oilfield,and the application results show that the method achieves effective identification of sand bodies around 4 m in a tight sandstone reservoir,reflecting the ability of this end-to-end intelligent modeling method to mine latent geological information from seismic attributes,thus confirming its effectiveness in quantitative reservoir prediction.

关 键 词：深度全连接神经网络 致密砂岩 储层参数 地震属性 有效砂体厚度 小样本 

分 类 号：P631.4[天文地球—地质矿产勘探] TP183[天文地球—地质学]