Identification of sensitive factors and desensitization application in shale oil reserve evaluation based on stochastic modeling: a case study of QY1 well group in the second member of Funing Formation, Qintong Sag, Subei Basin

MEI JUNWEI; XIONG XINYA; TANG JIANXIN; YANG ZHENGMAO; YAN YUZHU; ZAN LING

doi:10.13809/j.cnki.cn32-1825/te.20260110

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Identification of sensitive factors and desensitization application in shale oil reserve evaluation based on stochastic modeling: a case study of QY1 well group in the second member of Funing Formation, Qintong Sag, Subei Basin

更新时间：2026-08-04

- Identification of sensitive factors and desensitization application in shale oil reserve evaluation based on stochastic modeling: a case study of QY1 well group in the second member of Funing Formation, Qintong Sag, Subei Basin
- Petroleum Reservoir Evaluation and Development Pages: 1-11(2026)
- 作者机构：
  
  1.中国石化华东油气分公司，江苏南京 210019
  2.页岩油气富集机理与高效开发全国重点实验室，北京 102206
- 作者简介：
- 基金信息：
- DOI：10.13809/j.cnki.cn32-1825/te.20260110
  CLC： TE122
- Received：25 May 2026，
  
  Online First：04 August 2026，
- 稿件说明：
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梅俊伟, 熊欣雅, 唐建信, 等. 基于随机建模的页岩油储量评价敏感因素识别与降敏应用——以苏北盆地溱潼凹陷阜二段QY1井组为例[J]. 油气藏评价与开发, XXXX, XX(XX): 1-11.

Mei Junwei, Xiong Xinya, Tang Jianxin, et al. Identification of sensitive factors and desensitization application in shale oil reserve evaluation based on stochastic modeling： a case study of QY1 well group in the second member of Funing Formation, Qintong Sag, Subei Basin[J]. Petroleum Reservoir Evaluation and Development, XXXX, XX(XX): 1-11.
梅俊伟, 熊欣雅, 唐建信, 等. 基于随机建模的页岩油储量评价敏感因素识别与降敏应用——以苏北盆地溱潼凹陷阜二段QY1井组为例[J]. 油气藏评价与开发, XXXX, XX(XX): 1-11. DOI： 10.13809/j.cnki.cn32-1825/te.20260110.

Mei Junwei, Xiong Xinya, Tang Jianxin, et al. Identification of sensitive factors and desensitization application in shale oil reserve evaluation based on stochastic modeling： a case study of QY1 well group in the second member of Funing Formation, Qintong Sag, Subei Basin[J]. Petroleum Reservoir Evaluation and Development, XXXX, XX(XX): 1-11. DOI： 10.13809/j.cnki.cn32-1825/te.20260110.

摘要

地质储量的精准评价是认识页岩油藏开发潜力与制定科学开发方案的核心前提。针对页岩油储层非均质性强、参数预测多解性特征显著的问题，常规容积法掩盖了储层非均质性，传统概率法则低估了参数内在相关性，难以精准量化储量规模及不确定性。研究以苏北盆地溱潼凹陷阜宁组二段QY1页岩油井组为研究对象，采用随机建模方法开展储量评价全过程系统分析，重点完成敏感因素识别、降敏对策制定及储量评价应用。研究表明：地震解释层面深度偏差、孔隙度与含油饱和度建模主次变程参数及模型网格单元步长是影响储量不确定性的中-高敏感因素，其对储量的相对影响程度介于4.1~12.2%；通过构造建模层面的约束数据挖掘、属性建模的相控约束、模型网格步长优化等三大降敏对策，可将中-高敏感因素对储量的相对影响程度降低至2.4%~3.5%；降敏后评价储量分布值域区间收窄38.5%，大于累积概率曲线斜率增大14.7%，不确定性降低10.7%，储量预测精度明显提升。基于降敏后的随机建模方法进一步完成井组分层系储量量化评价，明确阜二段Ⅰ

下

—Ⅱ

上

亚段为储量分布核心区、Ⅲ亚段次之、Ⅳ—Ⅴ亚段极低；结合P50储量丰度与单井内部收益率关系，提出分层系井距优化及储层改造完善建议。

Abstract

Accurate evaluation of geological reserves is a core prerequisite for understanding the development potential of shale oil reservoirs and formulating scientific development plans. Aiming at the problems of strong heterogeneity of shale oil reservoirs

prominent non-uniqueness of parameter prediction

conventional volumetric method masking reservoir heterogeneity

traditional probabilistic method underestimating the inherent correlation of parameters

and difficulty in accurately quantifying reserve scale and uncertainty

this study takes the QY1 shale oil well group in the Second Member of Funing Formation

Qintong Sag

Subei Basin as the research object

adopts a stochastic modeling approach to conduct a systematic analysis of the entire reserve evaluation process

and focuses on completing the identification of sensitive factors

formulation of desensitization countermeasures

and application of reserve evaluation. The results show that: depth deviation in seismic interpretation

primary and secondary range parameters of porosity and oil saturation modeling

and model grid cell step size are medium-to-high sensitive factors affecting reserve uncertainty

with their relative impact on reserves ranging from 4.1 to 12.2%; through three major desensitization countermeasures — constrained data mining in structural modeling

facies-controlled constraint in property modeling

and optimization of model grid step size — the relative impact of medium-to-high sensitive factors on reserves can be reduced to 2.4% to 3.5%; after desensitization

the value range of evaluated reserve distribution narrows by 38.5%

the slope of the cumulative probability curve increases by 114.7%

uncertainty decreases by 10.7%

and reserve prediction accuracy is significantly improved. Based on the desensitized stochastic modeling method

the quantitative evaluation of reserves by formation in the well group was further completed

confirming that the interval from Lower Member Ⅰ to Upper Member Ⅱ is the core area of reserve distribution

followed by Member Ⅲ

and Submembers Ⅳ to Ⅴ with extremely low abundance; combined with the relationship between P50 reserve abundance and single-well internal rate of return

suggestions for optimization of well spacing by formation and improvement of reservoir stimulation were proposed.

关键词

Keywords

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