基于GWO-XGBoost模型的致密砂岩储层流体测井智能识别——以鄂尔多斯盆地洪德地区三叠系长8段为例

doi:10.12108/yxyqc.20260210

岩性油气藏 ›› 2026, Vol. 38 ›› Issue (2): 111–121.doi: 10.12108/yxyqc.20260210

基于GWO-XGBoost模型的致密砂岩储层流体测井智能识别——以鄂尔多斯盆地洪德地区三叠系长8段为例

薛博文¹^,²(), 张兆辉¹^,²(), 张皎生³, 邹建栋³, 张闻亭⁴

¹ 新疆大学新疆中亚造山带大陆动力学与成矿预测自治区重点实验室，乌鲁木齐 830017
² 新疆矿产资源绿色开发与生态修复省部共建协同创新中心，乌鲁木齐 830046
³ 中国石油长庆油田公司勘探开发研究院，西安 710018
⁴ 中国石油勘探开发研究院西北分院，兰州 730020

收稿日期:2025-09-01 修回日期:2025-10-15 出版日期:2026-03-01 发布日期:2025-12-15
第一作者:薛博文（1999—），男，新疆大学在读硕士研究生，研究方向为非常规储层测井评价方法。地址：（830049）新疆乌鲁木齐市水磨沟区华瑞街777号。Email:582724299@qq.com。
通信作者: 张兆辉（1982—），男，博士，副教授，主要从事测井地质学及非常规油气储层评价方面的教学与研究工作。Email:zhangzhaohui@xju.edu.cn。
基金资助:
国家自然科学基金项目“致密砂岩沉积层理地震响应机制及岩石相预测方法研究”(42464006);新疆维吾尔自治区“天池英才”计划项目“基于沉积-成岩补偿评价的致密砂岩储层甜点预测”(51052300560);甘肃省油气资源研究重点实验室开放基金项目“致密砂岩全尺度岩石相数字建模及其地震响应分析”(SZDKFJJ2023007)

Intelligent identification of fluid logging in tight sandstone reservoirs based on GWO-XGBoost model: A case study of Triassic Chang 8 member in Hongde area, Ordos Basin

XUE Bowen¹^,²(), ZHANG Zhaohui¹^,²(), ZHANG Jiaosheng³, ZOU Jiandong³, ZHANG Wenting⁴

¹ Xinjiang Key Laboratory for Geodynamic Processes and Metallogenic Prognosis of the Central Asian Orogenic Belt, Xinjiang University, Urumqi 830017, China
² Collaborative Innovation Center of Green Mining and Ecological Restoration for Xinjiang Mineral Resources, Urumqi 830046, China
³ Research Institute of Exploration and Development, PetroChina Changqing Oilfield Company, Xi’an 710018, China
⁴ PetroChina Research Institute of Petroleum Exploration and Development-Northwest, Lanzhou 730020, China

Received:2025-09-01 Revised:2025-10-15 Online:2026-03-01 Published:2025-12-15

摘要/Abstract

摘要：

针对传统测井解释方法在致密砂岩储层流体类型上识别精度低的问题，提出了一种基于测井曲线的GWO-XGBoost模型储层流体智能识别方法，并将该方法应用于鄂尔多斯盆地洪德地区三叠系长8段致密砂岩储层中。研究结果表明：①以鄂尔多斯盆地洪德地区三叠系长8段实际试油数据为目标变量，经主成分分析法优选出声波、自然电位、密度、井径、中子、自然伽马、电阻率测井（AT20、AT60和AT90）等9条测井曲线作为特征参数，再通过灰狼优化算法（GWO）对XGBoost模型的关键超参数进行全局优化。②GWO-XGBoost模型对储层流体类型的识别准确率达到96.55%，相较于XGBoost、随机森林（RF）和支持向量机（SVM）模型，其识别精度分别提升了6.03%，6.89%和22.41%，展现出明显的优势。③实际单井应用中，GWO-XGBoost模型通过对多维测井响应特征的综合分析与非线性特征学习，能够有效解决人工解释中低阻油层与高阻水层易混淆的难题，该模型在复杂储层条件下具有较高的稳定性与可靠性，可为提高致密砂岩油气勘探开发效率提供技术支撑。

关键词: XGBoost, 灰太狼算法（GWO）, 智能模型, 储层流体识别, 致密砂岩, 非常规油气, 三叠系, 洪德地区, 鄂尔多斯盆地

Abstract:

Traditional logging interpretation methods exhibit low accuracy in identifying fluid properties in tight sandstone reservoirs, in response to such problem, an intelligent identification method for reservoir fluid based on GWO-XGBoost model was proposed, and it was applied to Triassic Chang 8 tight sandstone reservoirs in Hongde area of Ordos Basin. The results show that: （1） Taking actual well testing data of Triassic Chang 8 tight sandstone reservoir in Hongde area of Ordos Basin as the target variable, nine logging curves, including acoustic, spontaneous potential, density, caliper, neutron, natural gamma, and three resistivity logs (AT 20, AT60, and AT90)，were selected as features parameters through principal component analysis. Then, key hyperpara-meters of XGBoost model were globally optimized using grey wolf optimization (GWO) algorithm. （2） GWO-XGBoost model has an accuracy of 96.55% in identifying reservoir fluid types, which is significantly higher than XGBoost, Random Forest (RF), and Support Vector Machine (SVM) models by 6.03%, 6.89%, and 22.41%, respectively. （3） In practical single-well applications, GWO-XGBoost model, through comprehensive analysis and nonlinear feature learning of multi-dimensional logging responses, effectively overcomes the common misclassification between low-resistivity oil layers and high-resistivity water layers in manual interpretation. This model exhibits high stability and reliability under complex reservoir conditions, providing effective technical support for improving the efficiency of tight sandstone oil and gas exploration and development.

Key words: XGBoost, grey wolf optimization algorithm （GWO）, intelligent model, reservoir fluid identification, tight sandstone, unconventional oil and gas, Triassic, Hongde area, Ordos Basin

中图分类号:

TE121.1

薛博文, 张兆辉, 张皎生, 邹建栋, 张闻亭. 基于GWO-XGBoost模型的致密砂岩储层流体测井智能识别——以鄂尔多斯盆地洪德地区三叠系长8段为例[J]. 岩性油气藏, 2026, 38(2): 111–121.

XUE Bowen, ZHANG Zhaohui, ZHANG Jiaosheng, ZOU Jiandong, ZHANG Wenting. Intelligent identification of fluid logging in tight sandstone reservoirs based on GWO-XGBoost model: A case study of Triassic Chang 8 member in Hongde area, Ordos Basin[J]. Lithologic Reservoirs, 2026, 38(2): 111–121.

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名称	PC1	PC2	PC3	综合得分系数	权重/%
AC	-0.197 580	-0.136 170	0.689 269	-0.014 130	14.18
AT 20	0.512 187	0.149 821	-0.009 810	0.243 170	10.37
AT60	0.520 014	0.130 652	0.023 080	0.244 804	10.43
AT 90	0.519 098	0.132 014	0.028 915	0.245 814	10.42
CAL	0.031 978	-0.465 760	0.080 078	-0.131 500	9.21
CNL	-0.074 120	0.469 021	0.447 027	0.198 049	12.67
DEN	-0.072 650	0.527 434	0.269 880	0.190 799	11.59
GR	0.188 145	-0.393 310	0.424 755	0.005 334	11.81
SP	0.334 004	-0.233 940	0.252 875	0.087 547	9.32

流体类型	标签	样本数	样本占比/%
水层	0	66	10.43
含油水层	1	64	10.11
油水同层	2	277	43.76
油层	3	226	35.70

基于GWO-XGBoost模型的致密砂岩储层流体测井智能识别——以鄂尔多斯盆地洪德地区三叠系长8段为例

Intelligent identification of fluid logging in tight sandstone reservoirs based on GWO-XGBoost model: A case study of Triassic Chang 8 member in Hongde area, Ordos Basin

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