基于双驱动模型的低渗气藏测井渗透率预测方法——以莺歌海盆地东方气田为例

doi:10.7657/XJPG20250513

摘要/Abstract

摘要：

海上低渗储集层岩性细、物性差、非均质性强，渗透率预测难。为此，建立了数据物理双驱动的回归委员会学习机（RCLM）进行低渗储集层测井渗透率预测，并在此基础上，开展储集层甜点评价及动态渗透率预测。结果表明：相对单一学习器，RCLM在保证预测精度的同时，模型预测稳定性更高；相对常规孔渗模型，RCLM预测的渗透率精度更高，在半个数量级内预测准确率达到94%；利用测井曲线及物性参数建立的测井甜点综合指数可以有效识别储集层甜点；新钻井验证了动静态渗透率转换模型具有较好的适用性，可用于区域滚动勘探开发中试井渗透率的预测。该方法在莺歌海盆地东方气田储集层评价中取得了良好的应用效果，有一定的推广应用价值，为海上气田高效勘探及开发方案编制提供有力支撑。

关键词: 莺歌海盆地, 东方气田, 低渗储集层, 测井渗透率, 动态渗透率, 甜点评价, 回归委员会学习机

Abstract:

Offshore low-permeability reservoirs are characterized by fine lithology, poor physical property, and strong heterogeneity, making permeability prediction highly challenging. To address this issue, a regression committee learning machine (RCLM) driven by both data and physics was developed for predicting permeability based on logging data for low-permeability reservoirs. On this basis, sweet spot evaluation and dynamic permeability prediction were conducted. The results show that compared with a simple learning machine, the RCLM not only guarantees the prediction accuracy but also achieves higher prediction stability; in comparison with conventional porosity-permeability models, the RCLM obtains superior accuracy (up to 94% within half an order of magnitude). The comprehensive logging-based sweet spot index established using logging curves and petrophysical parameters can be used to effectively identify sweet spots in reservoirs. The newly drilled wells have verified the applicability of the dynamic-static permeability transformation model, which can be used to predict well test-derived permeability during regionally progressive exploration and development. The proposed method has been successfully applied in reservoir evaluation in the Dongfang gas field of the Yinggehai Basin, demonstrating its practical value. This method may provide robust support for efficiently designing exploration and development plan for offshore gas fields.

Key words: Yinggehai Basin, Dongfang gas field, low-permeability reservoir, logging-based permeability, dynamic permeability, sweet spot evaluation, regression committee learning machine

中图分类号:

TE122.23

吴勃翰, 李芳, 汤翟, 吴一雄, 骆玉虎, 肖大志, 张顺超. 基于双驱动模型的低渗气藏测井渗透率预测方法——以莺歌海盆地东方气田为例[J]. 新疆石油地质, 2025, 46(5): 622-629.

WU Bohan, LI Fang, TANG Di, WU Yixiong, LUO Yuhu, XIAO Dazhi, ZHANG Shunchao. A Logging-Based Permeability Prediction Method Based on Dual-Driven Model for Low-Permeability Gas Reservoirs: A Case Study of Dongfang Gas Field in Yinggehai Basin[J]. Xinjiang Petroleum Geology, 2025, 46(5): 622-629.

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学习器	决定系数	均方根误差
RCLM	0.81~0.83 （0.82，0.005）	0.300~0.320 （0.310，0.004）
EMABP神经网络	0.73~0.83 （0.78，0.028）	0.310~0.370 （0.340，0.019）
RBF神经网络	0.79~0.82 （0.80，0.007）	0.310~0.320 （0.317，0.006）
ELM	0.72~0.78 （0.74，0.014）	0.340~0.380 （0.360，0.008）

孔隙度/ %	渗透率/ mD	含水饱和度/%	深电阻率与泥岩电阻率之比	解释结论	测井甜点综合指数
20	4.00	50	2.4	气层	53.67
15	1.00	50	2.4	气层	30.98
13	0.20	50	2.4	气层	14.88
15	1.00	75	1.2	气水同层	7.38
15	1.00	95	1.0	水层	1.30
10	0.05	95	1.1	干层	0.39

井区	动静态渗透率转换模型
DF1-1井区、DF11-2井区、 DF13-1井区、HK29-1井区	K_d=0.156 9（K_sC_p）^{1.661 2}
DF29-1井区	K_d=6.663 8（K_sC_p）^{0.565 2}
DF13-2井区	K_d=0.058 4（K_sC_p）^{0.565 2}
LD10-1井区	K_d=0.010 3（K_sC_p）^{2.442 6}