西安石油大学学报（自然科学版）2024，Vol.39Issue(2)：22-30,9.DOI:10.3969/j.issn.1673-064X.2024.02.003
基于神经网络模型的致密砂岩储层流动单元分类与评价
Classification and Evaluation of Flow Units in Tight Sandstone Reservoirs Based on Neural Network Model:A Case Study of Chang 6 Oil Reservoir in Northern WQ Area of Ordos Basin
摘要
Abstract
In order to clarify the impact of flow units in tight sandstone reservoirs in the northern WQ region of Ordos Basin on the dis-tribution of remaining oil,the basic characteristics of the Chang 6 reservoir in the study area were analyzed by means of core observa-tion,casting thin section identification,X-ray diffraction analysis,and high-pressure mercury injection testing.The types,characteristics,distribution patterns of flow units in Chang 6 reservoir and their relationship with the reservoir were analyzed in combination with logging data and neural network model analysis.The results show that the Chang 6 oil reservoir in the study area is typical low porosity-ultralow permeability reservoir,which is dominated by the rock types of feldspar sandstone and feldspar lithic sandstone.The pore types are mainly residual intergranular pore and feldspar dissolution pore,and characterized by the pore structures of fine throat-large pore and fine throat-small pore.Four types of reservoir flow units are developed in the Chang 6 oil reservoir in the study area.Type Ⅲ and Ⅳ flow units mainly developed in the Chang 62-1 sublayer,type Ⅰ and Ⅱ flow units mainly developed in the Chang 61-2 sublayer,and the Chang 61-1 sublayer only shows sporadic distribution of type Ⅰ flow unit.The favorable reservoirs are majorly distributed in the Chang 61-2 sublayer.关键词
流动单元/储层评价/致密砂岩/长6 油层组/鄂尔多斯盆地Key words
flow unit/reservoir evaluation/tight sandstone/Chang 6 oil reservoir/Ordos Basin分类
能源科技引用本文复制引用
崔英敏,杨江宇,饶利平,杨帅,蒋钧,路宽一,严昌鹏..基于神经网络模型的致密砂岩储层流动单元分类与评价[J].西安石油大学学报（自然科学版）,2024,39(2):22-30,9.基金项目
国家油气重大专项项目(2016ZX05046-002) （2016ZX05046-002）
