准噶尔盆地L区火山岩双重介质储层分类评价方法

doi:10.3969/j.issn.2096-1693.2025.01.014

石油科学通报 ›› 2025, Vol. 10 ›› Issue (3): 446-459. doi: 10.3969/j.issn.2096-1693.2025.01.014

准噶尔盆地L区火山岩双重介质储层分类评价方法

曹金鑫¹^,²(), 李宜强¹^,²^,^*(), 郑爱萍³, 李茂竹¹^,², 唐雪辰¹^,², 张雅倩¹^,², 刘哲宇¹^,²

1 中国石油大学(北京)石油工程学院，北京 102249
2 中国石油大学(北京)油气资源与工程全国重点实验室，北京 102249
3 中国石油新疆油田分公司重油开发公司，克拉玛依 834000

收稿日期:2025-02-11 修回日期:2025-05-09 出版日期:2025-06-15 发布日期:2025-07-30
通讯作者: *李宜强(1972年—)，博士，教授，主要从事复杂油藏提高采收率技术、CO₂驱油与埋存协同、非常规油气高效开发理论与技术等方面的研究，yiqiangli@cup.edu.cn。
作者简介:曹金鑫(2000年—)，博士研究生，主要从事非常规油藏提高采收率方面的研究，cjxafield@163.com。
基金资助:
国家自然科学基金 “多模态碎屑岩人造岩心孔隙结构控制理论与应用研究”(52074318)

Classification and evaluation methods of volcanic dual-media reservoirs in the L Block of the Junggar Basin

CAO Jinxin¹^,²(), LI Yiqiang¹^,²^,^*(), ZHENG Aiping³, LI Maozhu¹^,², TANG Xuechen¹^,², ZHANG Yaqian¹^,², LIU Zheyu¹^,²

1 College of Petroleum Engineering, China University of Petroleum, Beijing 102249, China
2 State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum, Beijing 102249, China
3 Heavy Oil Development Company, PetroChina Xinjiang Oilfield Company, Karamay 834000, China

Received:2025-02-11 Revised:2025-05-09 Online:2025-06-15 Published:2025-07-30

摘要/Abstract

摘要：

火山岩油藏是我国油气勘探的重要领域之一，受火山多期次活动及复杂成岩、构造作用的影响，火山岩储层表现出岩性多样、裂缝发育、微观非均质性强的特点，制约了对于该类储层的认识。明确双重介质下的储层特征对于火山岩油藏储集空间评价、开发方案优化和甜点区域选择具有重要的指导意义。针对上述问题本文提出了以产能为约束的双重介质储层定量分类评价思路。首先，以压汞测试所得的孔隙结构特征参数(孔隙半径均值、分选系数、偏态、峰态、变异系数)为基础，采用K-means聚类方法建立基质的分类标准，结合Shapley值以增强分类结果的可解释性。其次，以成像测井所得的裂缝密度为基础，采用K-means聚类方法建立裂缝分类标准。最后，综合考虑裂缝和基质的分类结果，以研究区不同类型储层最大单井月产量的总离差平方和最小为目标，采用粒子群算法优化双重介质储层的分类评价标准，并结合Arps产量递减规律分析不同类型储层的生产特征作为对分类结果的验证。结果表明，研究区主要发育以微裂缝类、沉凝灰岩类/凝灰质砂岩类、凝灰岩类及致密基质为主的4类物性逐渐变差的储层，影响基质储层分类结果的因素依次为渗透率、孔隙半径均值、偏态和孔隙度。按照裂缝密度11.35、6.14、3.03条/m为界限可将研究区裂缝储层分为4类。裂缝－基质综合分类结果表明渗流能力和储集能力都是影响火山岩双重介质储层开发的重要因素，裂缝类别、基质类别与综合类别的Pearson相关系数分别为0.63和0.69。研究区生产井符合Arps指数递减规律，初始产量和递减率呈现正相关的关系，随着储层物性变差，油井呈现出初始产量降低、递减率降低的变化趋势。本文不仅为火山岩储层的分类提供了定量的认识，也为其他双重、三重介质以及需要从多个角度进行评价的储层提供了分析的思路。

关键词: 火山岩, 双重介质, 储层分类, 准噶尔盆地, 石炭系

Abstract:

Volcanic reservoirs are important fields for oil and gas exploration in China. Affected by multiple volcanic activities and complex diagenesis and tectonic processes, these reservoirs exhibit diverse lithology, well-developed fractures, and strong micro-heterogeneity, which restrict the understanding of such reservoirs. Clearly defining the reservoir characteristics under dual-media conditions is of great guiding significance for the evaluation of storage capacity, optimization of development strategies, and identification of sweet spots in volcanic rock oil reservoirs. To address these challenges, this paper proposes a quantitative classification and evaluation approach for dual-media reservoirs constrained by productivity. Firstly, based on the pore structure characteristic parameters obtained from mercury injection tests (mean pore radius, sorting coefficient, skewness, kurtosis, and coefficient of variation), the K-means clustering method is employed to establish the classification criteria for the matrix, then the Shapley value is incorporated to improve the interpretability of the classification results. Secondly, fracture classification criteria are established using the K-means clustering method based on fracture density derived from electrical micro-imaging (EMI) logs. Finally, by integrating the fracture and matrix classification results, we employ the particle swarm optimization algorithm to optimize the dual-media reservoir classification criteria. The optimization objective is to minimize the total sum of squared deviations of maximum monthly production among different reservoir types within the study area. The classification results are further validated through production characteristic analysis of different reservoir types based on Arps production decline model. The results show that four types of reservoirs with gradually deteriorating physical properties, namely, micro-fracture type, condensed tuff type/tuffaceous sandstone type, tuff type, and tight matrix type, are mainly developed in the study area. The factors influencing the matrix reservoir classification results are, in descending order of significance: permeability, mean pore radius, skewness, and porosity. The fractured reservoirs are classified into four categories based on fracture density thresholds of 11.35, 6.14, and 3.03 fractures/m. Comprehensive classification results reveal that both seepage capacity and storage capacity significantly impact reservoir performance. The Pearson correlation coefficient between the fracture category and comprehensive category is 0.63, while that between the matrix category and comprehensive category is 0.69. Production analysis demonstrates that all wells follow Arps’ exponential decline model, showing a positive correlation between initial production and decline rates. As reservoir quality declines, oil wells exhibit both lower initial production and slower decline rates. This paper not only provides a quantitative understanding of the classification of volcanic rock reservoirs but also offers a methodological reference for characterizing other dual- and triple-media reservoirs requiring multi-dimensional evaluation approaches.

Key words: volcanic rock, dual media, reservoir classification, Junggar Basin, Carboniferous system

中图分类号:

曹金鑫, 李宜强, 郑爱萍, 李茂竹, 唐雪辰, 张雅倩, 刘哲宇. 准噶尔盆地L区火山岩双重介质储层分类评价方法[J]. 石油科学通报, 2025, 10(3): 446-459.

CAO Jinxin, LI Yiqiang, ZHENG Aiping, LI Maozhu, TANG Xuechen, ZHANG Yaqian, LIU Zheyu. Classification and evaluation methods of volcanic dual-media reservoirs in the L Block of the Junggar Basin[J]. Petroleum Science Bulletin, 2025, 10(3): 446-459.

https://sykxtb.cup.edu.cn/CN/Y2025/V10/I3/446

图/表 14

图1 研究区构造位置^[45]

Fig. 1 The structural location of the study area

图2 研究区典型岩心、铸体薄片图

Fig. 2 Typical core and cast thin section of the study area

图3 双重介质储层分类技术路线

Fig. 3 Technical workflow of dual medium reservoir classification

图4 EMI成像测井显示的裂缝特征

Fig. 4 Fracture characteristics displayed by EMI imaging logs

图5 基于K-means的孔隙结构分类结果

Fig. 5 Classification results of pore structure based on K-means

图6 不同类型储层分类参数分布图

Fig. 6 Distribution of classification parameters for different types of reservoirs

表1 不同类型储层分类参数界限

Table 1 Classification parameter limits for different types of reservoirs

类别	孔隙度/%	渗透率/mD	孔隙半径均值/μm	分选系数	偏态	峰态	变异系数
Ⅰ	(5.05~12.2) 8.38	(0.193~7.190) 2.565	(0.211~0.040) 0.089	(0.55~2.86) 1.72	(-3.36~-0.74) (-1.38)	(2.04~13.06) 3.94	(0.04~0.25) 0.140
Ⅱ	(4.24~11.26) 6.63	(0.010~2.800) 0.167	(0.091~0.037) 0.049	(0.27~1.89) 0.96	(-4.82~-0.86) (-2.48)	(2.20~25.05) 8.20	(0.02~0.15) 0.073
Ⅲ	(4.10~5.38) 4.39	(0.010~0.299) 0.037	(0.052~0.036) 0.039	(0.26~1.27) 0.50	(-5.16~-1.97) (-4.00)	(5.12~28.72) 18.58	(0.02~0.10) 0.036
Ⅳ	(4.17~5.32) 4.46	(0.004~0.009) 0.008	(0.041~0.037) 0.038	(0.27~0.68) 0.47	(-8.26~-3.04) (-4.71)	(10.27~74.84) 26.81	(0.02~0.05) 0.035

图7 绝对夏普利值的均值与分类参数的关系

Fig. 7 Relationship between mean absolute Shapley value and classification parameters

图8 不同类型储层分类结果的夏普利摘要及典型孔隙结构铸体薄片图

Fig. 8 Shapley summary and typical pore structure cast thin images of classification results for various reservoirs

表2 不同类型裂缝储层分级标准

Table 2 Classification criteria for different types of fractured reservoirs

储层类别	裂缝密度/(条/m)	流体流动难度
Ⅰ	>11.35	极易
Ⅱ	6.14~11.35	易
Ⅲ	3.03~6.14	难
Ⅳ	<3.03	极难

图9 研究区裂缝密度分布图

Fig. 9 Distribution of fracture density in the study area

图10 裂缝-基质双重介质储层综合分类标准

Fig. 10 Comprehensive classification criteria for fracture-matrix dual medium reservoirs

图11 研究区双重介质储层分类结果

Fig. 11 Classification results of dual medium reservoirs in the target area

图12 储层分类结果的验证

Fig. 12 Verification of reservoir classification results

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准噶尔盆地L区火山岩双重介质储层分类评价方法

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