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高校地质学报 ›› 2024, Vol. 30 ›› Issue (04): 496-504.DOI: 10.16108/j.issn1006- 7493.2023042

• • 上一篇    

准噶尔盆地玛中地区砂砾岩储层特征及非线性反演方法的应用

张小栓1, 张 欣1, 廖启平1,樊亚飞1,刘谨铭1,魏东涛2*   

  1. 1. 中国石油新疆油田分公司百口泉采油厂,克拉玛依 834000;
    2. 中国地质调查局西安地质调查中心,西安 710119
  • 出版日期:2024-08-20 发布日期:2024-08-20

The Characteristics of Sandy Conglomerate Reservoir in Mazhong Area of the Junggar Basin and A Nonlinear Inversion Method be Used to Predict It

ZHANG Xiaoshuan1,ZHANG Xin1,LIAO Qiping1,FAN Yafei1,LIU jinming1,WEI Dongtao2*   

  1. 1. Baikouquan Oil Production Plant of Xinjiang Oilfield Company, PetroChina, Karamay 834000, China;
    2. Xi’an Center of Geological Survey,China Geological Survey,Xi’an 710119,China
  • Online:2024-08-20 Published:2024-08-20

摘要: 随着近年来在玛湖凹陷斜坡区下三叠统百口泉组砂砾岩储层中发现越来越多的油气藏,有关砂砾岩优质储层的预测技术也受到很多学者的关注。文章分析了研究区砂砾岩储层的岩石铸体薄片、矿物成分分析和氦气孔隙度测量结果,发现塑性碎屑含量对该类岩石的物性有较大的影响,随着岩石中塑性碎屑含量的增加,岩石的孔隙度和渗透率都会降低。同时,利用测井数据开展了优质储层的敏感参数优选,优质储层在常规测井曲线上表现为低自然伽马和高电阻率的特征,并构建了可以表征塑性碎屑含量的特征曲线,该曲线可以有效识别研究区的优质储层。利用人工智能反演技术在研究区开展了基于该特征曲线的非线性反演,结合敏感曲线交会分析认为,当特征曲线小于0.3的地区是研究区优质砂砾岩储层的发育区。

关键词: 特征曲线, 塑性碎屑, 自然伽马, 电阻率, 砂砾岩储层

Abstract: With the discovery of more and more reservoirs in the Lower Triassic Baikouquan Formation sandy conglomerate reservoirs in the slope area of Mahu Depression in recent years, prediction techniques for high-quality sandy conglomerate reservoirs have also attracted the attention of many scholars. This article analyzes the rock casting thin sections, mineral composition analysis, and helium gas porosity measurement results of sandy conglomerate reservoirs in the study area. It is found that the content of plastic debris has a significant impact on the porosity and permeability of the rock. As the content of plastic debris in the rock increases, the porosity and permeability of the rock will decrease. At the same time, sensitive parameter optimization of high-quality reservoirs was carried out using logging data. High quality reservoirs exhibited low natural gamma and high resistivity characteristics on conventional logging curves, and a characteristic curve was constructed to characterize the content of plastic debris. This curve can effectively identify high-quality reservoirs in the study area. In this paper, a nonlinear inversion method using artificial intelligence inversion will be introduced, and we will use this method to predicate the characteristic curve in the study area. When the value of the characteristic curve is less than 0.3, we can have a the area is considered have high-quality reservoir.

Key words: characteristic curve, plastic debris, Natural gamma logging, Resistivity logging, Sandy conglomerate reservoir

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