Study on distribution of tight gas reservoirs in shallow-water delta fronts based on well-seismic intelligent prediction: a case study of the Permian Shanxi Formation in Yan’an Gas Field,Ordos Basin

doi:10.7605/gdlxb.2026.008

Journal of Palaeogeography（Chinese Edition） ›› 2026, Vol. 28 ›› Issue (1): 333-352. doi: 10.7605/gdlxb.2026.008

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Study on distribution of tight gas reservoirs in shallow-water delta fronts based on well-seismic intelligent prediction: a case study of the Permian Shanxi Formation in Yan’an Gas Field,Ordos Basin

WANG Xiangzeng¹(), QIAO Xiangyang¹, XIN Cuiping¹, XU Zhenhua²^,³(), WU Hongli²^,³, LI Kexin²^,³, SUN Jianfeng¹, SONG Jiaxuan¹

1 Shaanxi Yanchang Petroleum(Group)Co.,Ltd.,Xi’an 710065,China
2 State Key Laboratory of Petroleum Resources and Engineering,China University of Petroleum(Beijing),Beijing 102249,China
3 College of Geosciences,China University of Petroleum(Beijing),Beijing 102249,China

Received:2025-09-30 Revised:2025-11-03 Online:2026-02-01 Published:2026-02-09
Contact: XU Zhenhua,born in 1992,holds a doctoral degree and the titles of associate professor and master’s supervisor. He is mainly engaged in research on oil and gas field development geology. E-mail: xuzhenhua@cup.edu.cn.
About author:
WANG Xiangzeng,born in 1968,holds a doctoral degree and the title of professor-level senior engineer,academician of the Chinese Academy of Engineering. He is mainly engaged in research on the exploration and development of low-permeability oil and gas fields. E-mail: sxycpcwxz@126.com.
Supported by:
Foundation of China University of Petroleum(Beijing)(2462025BJRC005); National Natural Science Foundation of China(42202178)

基于井震智能预测的浅水三角洲前缘致密气储集层分布规律研究: 以鄂尔多斯盆地延安气田山西组为例^*

王香增¹(), 乔向阳¹, 辛翠平¹, 徐振华²^,³(), 吴红丽²^,³, 李可心²^,³, 孙建峰¹, 宋珈萱¹

1 陕西延长石油(集团)有限责任公司,陕西西安 710065
2 油气资源与工程全国重点实验室,中国石油大学(北京),北京 102249
3 中国石油大学(北京)地球科学学院,北京 102249

通讯作者: 徐振华,男,1992年生,博士、副教授、硕士生导师,主要从事油气田开发地质研究。E-mail: xuzhenhua@cup.edu.cn。
作者简介:
王香增,男,1968年生,博士、教授级高级工程师,中国工程院院士, 主要从事低渗油气田勘探开发研究。E-mail: sxycpcwxz@126.com。
基金资助:
*中国石油大学（北京）科研基金(2462025BJRC005); 国家自然科学基金(42202178)

Abstract

Abstract:

The Permian Shanxi Formation in the Yan’an Gas Field of the Ordos Basin contains tight sandstone gas reservoirs within a shallow delta front. However,predicting sandbody distribution is challenging due to strong reservoir heterogeneity,large well spacing,and the signal-attenuating effects of the Quaternary loess plateau. As a result,the distribution patterns of effective gas-bearing reservoirs remain poorly understood. Using the S2³ submember of the Shanxi Formation in the Gaojiahe area of the central Yan’an Gas Field as a case study,this research proposes an intelligent sandbody prediction method based on an adaptive weighting strategy. This approach integrates the relationship between amplitude-frequency characteristics and tuned thickness into an attention mechanism,establishing an adaptive weighting framework for multi-band seismic attributes. The weighting strategy prioritizes high-frequency attributes for identifying thin layers and low-frequency attributes for thick layers,effectively addressing sandbody prediction challenges in the presence of low-quality seismic data. Compared with existing methods,the proposed approach yields the most accurate sandbody thickness predictions across various ratios of learning wells to blind wells. For example,in a test involving 45 learning wells and 63 blind wells,the correlation coefficient(R) between predicted and actual sandbody thickness reached 0.85. Prediction results indicate that the S2³ submember in the Gaojiahe area develops wide,banded sand bodies deposited in a shallow-water delta-front environment. Thick,coarse-grained sediments in the main distributary channels,along with the main body and inner edge of the mouth bar,are confined within the palaeo-geomorphological low of the main valley. Due to wave scouring,these intervals exhibit high quartz content and represent the principal effective gas reservoirs.

Key words: shallow-water delta front, effective reservoir, intelligent attribute fusion, valley palaeogeomorphology, Yan’an Gas Field, Shanxi Formation, Ordos Basin

摘要：

鄂尔多斯盆地延安气田二叠系山西组发育浅水三角洲前缘致密砂岩气储集层,然而,由于储集层非均质性强、井距大以及第四系黄土塬的影响,导致砂体分布预测难度大,有效含气储集层分布规律尚不明确。本研究以延安气田中部高家河地区山西组2³亚段为例,提出了一套基于自适应权重策略的井震砂体智能预测方法,即将振幅—频率与调谐厚度的关系引入注意力机制,构建了多频段地震多属性的自适应权重分配机制,达到高频属性聚焦薄层、低频属性聚焦厚层的智能权重分配策略,以解决低品质地震资料条件下砂体预测难题。与其他已有方法相比,考虑不同学习井—盲井比例,本次方法的砂体厚度分布预测效果均最好,其中,学习井45口、盲井63口情况下,盲井的预测与实际砂厚的相关系数(R)达0.85。砂体预测结果表明,高家河地区山2³亚段发育宽条带状海相浅水三角洲前缘砂体,厚层、粗粒的主分流河道、河口坝主体与内缘受限于主沟谷古地貌内,受海浪淘洗作用,石英含量高,为主要的有效含气储集层。

关键词: 浅水三角洲前缘, 有效储集层, 智能属性融合, 古沟谷地貌, 延安气田, 山西组, 鄂尔多斯盆地

CLC Number:

P539.2

WANG Xiangzeng, QIAO Xiangyang, XIN Cuiping, XU Zhenhua, WU Hongli, LI Kexin, SUN Jianfeng, SONG Jiaxuan. Study on distribution of tight gas reservoirs in shallow-water delta fronts based on well-seismic intelligent prediction: a case study of the Permian Shanxi Formation in Yan’an Gas Field,Ordos Basin[J]. Journal of Palaeogeography（Chinese Edition）, 2026, 28(1): 333-352.

王香增, 乔向阳, 辛翠平, 徐振华, 吴红丽, 李可心, 孙建峰, 宋珈萱. 基于井震智能预测的浅水三角洲前缘致密气储集层分布规律研究: 以鄂尔多斯盆地延安气田山西组为例^*[J]. 古地理学报, 2026, 28(1): 333-352.

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URL: http://www.gdlxb.cn/EN/10.7605/gdlxb.2026.008

http://www.gdlxb.cn/EN/Y2026/V28/I1/333

Figures/Tables 19

Fig.1 Structural position,stratigraphic column,regional position and sand thickness of the Yan’an Gas Field,Ordos Basin

Fig.2 Well locations,seismic profiles,and seismic frequency maps in the Yan’an Gas Field,Ordos Basin

Fig.3 Typical core photos of the S2³ submember in Yan’an Gas Field,Ordos Basin

Fig.4 Workflow of sand body prediction method based on the adaptive weight strategy

Table 1 Correlation between different single seismic attributes and sand body thickness

地震属性	原始地震属性与砂厚相关系数(R)
平均负振幅	0.35
平均正振幅	0.02
中值振幅	0.01
平均绝对振幅	0.24
最大绝对振幅	0.14
总负振幅	0.20
总正振幅	0.01
总振幅	0.20
总绝对振幅	0.14
均方根振幅	0.13
平均能量	0.10
总能量	0.05
总绝对能量	0.10
半能量	0.36
平均瞬时频率	0.18
平均瞬时相位	0.11

Fig.5 Normalized amplitude seismic at different fractional frequencies in the S2³ submember of Yan’an Gas Field,Ordos Basin

Table 2 Frequency and tuning thickness of different frequency-fractionated seismic bodies in the Yan’an Gas Field,Ordos Basin

分频主频/Hz	20	25	30	35	40	45	50	55	60
实际主频/Hz	23	26.5	30.5	34.5	39	42.5	44.5	46.5	46.5
调谐厚度/m	32.6	25.9	21.6	18.6	16.2	14.3	13.1	11.8	10.8

Fig.6 Planar distribution of nine seismic attributes extracted based on preferred attributes and frequency bands for the S2³ submember in Yan’an Gas Field,Ordos Basin

Fig.7 Loss function diagram of the machine learning model based on adaptive weight strategy

Fig.8 Comparison of fused seismic attributes and predicted sand body thickness for the S2³ submember of Shanxi Formation in Yan’an Gas Field,Ordos Basin

Fig.9 Correlation diagram of actual sand body thickness,fusion attributes and predicted sand body thickness based on blind well test set in the Yan’an Gas Field,Ordos Basin

Fig.10 Well logging interpretation template for different sedimentary microfacies in the S2³ submember in Yan’an Gas Field,Ordos Basin

Fig.11 Sedimentary microfacies and effective sand body thickness planar graph of the S2³ submember of Yan’an Gas Field,Ordos Basin

Fig.12 Photographs of typical sandstone thin sections identified in the S2³ submember of Yan’an Gas Field,Ordos Basin

Fig.13 Planar graph of fused seismic attributes considering different training-test set ratios

Fig.14 Comparison chart of the correlation coefficient R for sand body prediction based on four training-test set ratios and five intelligent fusion methods

Fig.15 MAE and RMSE of different models on the training and test sets

Fig.16 Isopach map of the Taiyuan Formation in Yan’an Gas Field,Ordos Basin and superimposed map of sand body and effective sand body distribution with palaeogeography of the Taiyuan Formation and the S2³ submember

Fig.17 Scatter plot of the Taiyuan Formation thickness vs. the effective sand body thickness of the S2³ submember in Yan’an Gas Field,Ordos Basin

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Study on distribution of tight gas reservoirs in shallow-water delta fronts based on well-seismic intelligent prediction: a case study of the Permian Shanxi Formation in Yan’an Gas Field,Ordos Basin

基于井震智能预测的浅水三角洲前缘致密气储集层分布规律研究: 以鄂尔多斯盆地延安气田山西组为例^*

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