The ambiguity-resolution problem and analytical techniques of the primary controlling factors for fine-grained rock pore characteristics

Dong Lin; Wang Ziyi; Mei Qiliang; Zhu Rukai; Jin Zhijun

doi:10.7623/syxb202606005

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The ambiguity-resolution problem and analytical techniques of the primary controlling factors for fine-grained rock pore characteristics

更新时间：2026-07-10

- The ambiguity-resolution problem and analytical techniques of the primary controlling factors for fine-grained rock pore characteristics
- ACTA PETROLEI SINICA Vol. 47, Issue 6, Pages: 1190-1203(2026)
- 作者机构：
  
  1. 北京大学造山带与地壳演化教育部重点实验室,北京,100871
  2. 北京大学地球与空间科学学院,北京,100871
  3. 中国石油长庆油田公司,陕西,西安,710018
  4. 低渗透油气田勘探开发国家工程实验室,陕西,西安,710018
  5. 北京大学能源研究院,北京,100871
  6. 中国石油勘探开发研究院,北京,100083
- 作者简介：
- 基金信息：
- DOI：10.7623/syxb202606005
  CLC： TE122.2
- Online First：02 July 2026，
  
  Published：2026
- 稿件说明：
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董琳, 王梓毅, 梅启亮, 朱如凯, 金之钧. 细粒岩孔隙主控因素多解性问题与解析技术[J]. 石油学报, 2026, 47(6): 1190-1203,1257.

Dong Lin, Wang Ziyi, Mei Qiliang, et al. The ambiguity-resolution problem and analytical techniques of the primary controlling factors for fine-grained rock pore characteristics[J]. ACTA PETROLEI SINICA, 2026, 47(6): 1190-1203.
董琳, 王梓毅, 梅启亮, 朱如凯, 金之钧. 细粒岩孔隙主控因素多解性问题与解析技术[J]. 石油学报, 2026, 47(6): 1190-1203,1257. DOI： 10.7623/syxb202606005.

Dong Lin, Wang Ziyi, Mei Qiliang, et al. The ambiguity-resolution problem and analytical techniques of the primary controlling factors for fine-grained rock pore characteristics[J]. ACTA PETROLEI SINICA, 2026, 47(6): 1190-1203. DOI： 10.7623/syxb202606005.

摘要

精准刻画细粒岩孔隙特征并揭示其主控因素

是制定非常规油气高效勘探开发方案的重要科学基础。传统研究常基于孔隙参数(如比孔容、分形维数等)和地质变量(如总有机碳、矿物组成等)的相关性分析来推断主控因素。然而

单纯的统计学关联难以揭示变量间复杂的成因联系

常引发地质解释的多解性

为准确揭示孔隙发育机理带来挑战。针对这一问题

系统梳理了细粒岩孔隙特征主控因素的多解性问题及其解析技术的研究进展

重点总结了4方面成果和认识:①地质变量间的多重共线性是引发孔隙参数与非主控因素间形成伪相关性的内在机制

传统相关性分析在主控因素研究中存在固有方法的局限;②在鄂尔多斯盆地三叠系延长组7段开展的技术应用表明

将孔隙子群体定量表征与传统相关性分析相结合

可以解析伪相关引发的地质解释多解性

增强主控因素分析的可靠性;③采用基于通用方程的改进累积尺寸分布模型和基于孔隙自相似性原理的小尺度介孔预测模型

可以助力多解性解析方法在孔隙分形领域和介孔尺度研究中的有效应用;④当前的多解性解析技术仍存在不足

在未来的研究中融合成因推断等机器学习方法

有望进一步提升该技术的应用效果与智能化水平。研究旨在为细粒岩孔隙评价和成储机制研究提供理论参考与技术启示。

Abstract

Accurate characterization of pore systems in fine-grained rocks and the elucidation of their primary controlling factors provide a vital scientific basis for formulating efficient unconventional hydrocarbon exploration and development strategies. Conventional research often infers dominant controlling factors through correlation analysis between pore parameters (e.g.

specific pore volume

fractal dimension) and geological variables (e.g.

total organic carbon

mineral composition). However

simplistic statistical associations fail to reveal the intricate genetic linkages among variables

leading to ambiguity in geological interpretations and posing significant challenges to the precise deciphering of pore development mechanisms. To address this issue

this study systematically reviews the research progress regarding the ambiguity of primary controlling factors for fine-grained rock pore characteristics and their corresponding analytical techniques

highlighting four key advancements and insights. (1) Multicollinearity among geological variables is the intrinsic mechanism driving spurious correlations between pore parameters and non-dominant factors

illustrating the inherent methodological limitations of conventional correlation analysis in identifying primary controlling factors. (2) Empirical analyses of the Member 7 of Triassic Yanchang Formation in Ordos Basin demonstrate that integrating the quantitative characterization of pore subpopulations with traditional correlation analysis can resolve the ambiguity in geological interpretations caused by spurious correlations

thereby enhancing the reliability of primary controlling factor identification. (3) Utilizing an improved cumulative size distribution model derived from general equations alongside a small-scale mesopore prediction model based on pore self-similarity principles facilitates the effective application of ambiguity-resolution methods in the research of pore fractals and mesopore sizes. (4) Current ambiguity-resolution techniques still exhibit limitations. Integrating machine learning approaches

such as causal inference

into future research holds promise for further enhancing the practical efficacy and intelligence of these techniques. This study aims to provide theoretical references and technical insights for the evaluation of fine-grained rock pores and the investigation of reservoir-formation mechanisms.

关键词

Keywords

references

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