石油与天然气地质 ›› 2025, Vol. 46 ›› Issue (5): 1717-1730.doi: 10.11743/ogg20250520

• 方法技术 • 上一篇    

低渗透-致密储层分类评价方法——以鄂尔多斯盆地三叠系延长组下组合为例

沈卫民1,2,3(), 赵靖舟1,2(), 赵美丽3   

  1. 1.西安石油大学 地球科学与工程学院，陕西 西安 710065
    2.西安石油大学 陕西省油气成藏地质学重点实验室，陕西 西安 710065
    3.中国石化 江苏油田分公司 采油二厂，江苏 淮安 211600
  • 收稿日期:2025-07-02 修回日期:2025-08-27 出版日期:2025-10-30 发布日期:2025-10-29
  • 通讯作者: 赵靖舟 E-mail:2642353946@qq.com;jzzhao@xsyu.edu.cn
  • 第一作者简介:沈卫民（1997—），男，硕士研究生，地质工程。E-mail： 2642353946@qq.com。
  • 基金项目:
    国家科技重大专项(2016ZX05050)

Methods for classification and evaluation of low-permeability tight reservoirs： A case study of the lower Yanchang Formation， Ordos Basin

Weimin SHEN1,2,3(), Jingzhou ZHAO1,2(), Meili ZHAO3   

  1. 1.School of Earth Sciences and Engineering，Xi’an Shiyou University，Xi’an，Shaanxi 710065，China
    2.Shaanxi Key Laboratory of Petroleum Accumulation Geology，Xi’an Shiyou University，Xi’an，Shaanxi 710065，China
    3.No. 2 Oil Production Plant，Jiangsu Oilfield Company，SINOPEC，Huai’an，Jiangsu 211600，China
  • Received:2025-07-02 Revised:2025-08-27 Online:2025-10-30 Published:2025-10-29
  • Contact: Jingzhou ZHAO E-mail:2642353946@qq.com;jzzhao@xsyu.edu.cn

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摘要：

低渗透-致密储层的分类评价是油气勘探开发中亟待解决的难题。传统的方法要么只适用于常规储层，要么对低渗透-致密储层的分类过于简单，从而难以满足低渗透-致密储层勘探开发的实际需要；一些新提出的评价方法虽然应用效果较好，但参数获取难度大，成本高，难于推广。在对研究区储层基本特征进行铸体薄片分析、扫描电镜观察及高压压汞实验等研究的基础上，结合大量数据处理和不同聚类分析方法，建立了基于K均值算法聚类分析的低渗透-致密储层分类评价方法，并借助孔-渗交会图实现了分类界限的数学表征。新建立的分类评价体系具有以下特点和优势：①通过算法驱动突破了传统储层经验分类评价标准的局限性，使分类更加科学。②综合运用反映储层物性和孔隙结构特征的8个主要参数（孔隙度、渗透率、分选系数、中值压力、中值半径、排驱压力、最大进汞饱和度和退汞效率）进行储层分类评价，但最终结果仅用最能反映储层品质、也最易获取的孔隙度和渗透率2个参数来进行表征，从而避免了大多数多参数储层评价参数获取难、不易推广应用的不足。③建立了以数学函数为边界的评价方法，从而克服了传统“一刀切”的简单划分做法。运用该方法对鄂尔多斯盆地定边—富县地区三叠系延长组下组合长7—长9油层组低渗透-致密储层进行了分类评价和有利储层分布区预测，为该地区甜点评价提供了依据。

关键词: 分类, 评价, 聚类分析, 低渗透, 致密, 储层, 延长组, 鄂尔多斯盆地

Abstract:

The classification and evaluation of low-permeability tight reservoirs remain a pressing challenge in hydrocarbon exploration and exploitation. Traditional approaches are either tailored to conventional reservoirs or oversimplify the categorization of low-permeability tight reservoirs， limiting their practical applicability. Although some recently developed evaluation methods have shown promising results， their broad adoption is hindered by difficulties in parameter acquisition and high implementation costs. In this study， we examine the general reservoir characteristics in the study area using casting thin section observations， scanning electron microscopy （SEM）， and high-pressure mercury injection （HPMI）. Based on these results， combined with extensive data processing and multiple clustering algorithms， we propose a K-means clustering-based classification and evaluation method for low-permeability tight reservoirs. Furthermore， we mathematically define the classification boundaries using porosity-permeability cross plots. The proposed classification and evaluation system offers several advantages. By adopting an algorithm-driven approach， it overcomes the limitations of traditional experience-based criteria， thus providing more scientifically robust classification results. Although the proposed method integrates eight key parameters that capture reservoir physical properties and pore structure characteristics （i.e.， porosity， permeability， sorting coefficient， median pressure， median pore radius， displacement pressure， maximum mercury saturation， and mercury withdrawal efficiency）， the final classification and evaluation results rely solely on porosity and permeability， which are both the most indicative of reservoir quality and the most accessible. Therefore， this method addresses the limitations of traditional ones， including difficulty in acquiring evaluation parameters and challenges associated with widespread application. Employing mathematically defined classification boundaries， it avoids the oversimplified “one-size-fits-all” cut-offs inherent to traditional classifications. The method has been applied to the classification and play fairway prediction of low-permeability tight reservoirs in the Chang 7-9 oil groups in the lower Yanchang Formation， Dingbian-Fuxian area， Ordos Basin， providing a reliable basis for sweet spot evaluation in this area.

Key words: classification, evaluation, cluster analysis, low permeability, tightness, reservoir, Yanchang Formation, Ordos Basin

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