摘要
页岩气已成为中国油气资源的重要战略接替领域,页岩具有低孔、低渗特征,只有经过大规模体积压裂才能获得工业产能,页岩压裂后的裂缝参数精细刻画及定量表征是压裂效果评价和开发参数优化的关键。为此,以页岩岩心压裂后三维CT图像为研究对象,开展基于深度学习语义分割模型的裂缝智能提取。首先,构建融合金字塔卷积与注意力机制的U-Net深度学习模型,减轻图像类别失衡的影响,提升裂缝提取的精确度;其次,基于语义分割结果建立数字岩心模型,结合孔隙度、倾斜指数等参数实现裂缝空间分布的定量表征;最后,通过多重分形谱中的谱峰及谱宽表征缝网复杂度。研究结果表明:相较于传统图像分割模型,改进后模型的灵敏度提升了6.69%,交并比提升了0.48%。通过图像分割算法优化、数字岩心建模及多重分形分析,系统刻画了三维裂缝特征,适用于页岩等非常规储层缝网表征方法可为水力压裂后储层改造效果评估提供参照。
Shale gas has become an important strategic alternative field for China’s oil and gas resources.Shale is characterized by low porosity and low permeability,and only after going through large-scale volume fractu-ring can industrial production capacity be obtained.The fine characterization and quantitative characterization of fracture parameters after shale fracturing are the key to fracturing effect evaluation and development parameter optimization.By taking the three-dimensional CT images of shale cores after fracturing as the research object,this paper conducts intelligent fracture extraction based on the deep learning semantic segmentation model.Firstly,a U-Net deep learning model integrating the pyramid convolution and attention mechanism is built to al-leviate the influence of image category imbalance and improve fracture extraction accuracy.Secondly,a digital core model is built based on the semantic segmentation results,and quantitative characterization of the spatial distribution of fractures is realized by combining parameters such as the porosity and tilt index.Finally,the complexity of the fracture network is characterized by the peak and width of the multi-fractal spectrum.The re-search results show that compared with the traditional image segmentation model,the sensitivity of the im-proved model is increased by 6.69%,and the intersection over union grows by 0.48%.This study systemati-cally characterizes the three-dimensional fracture features by image segmentation algorithm optimization,digital core modeling,and multi-fractal analysis,which is applicable to the characterization of fracture networks in un-conventional reservoirs such as shale and can provide a reference for the evaluation of reservoir stimulation ef-fects after hydraulic fracturing.
作者
王飞
黄露逸
边会媛
程茜
WANG Fei;HUANG Luyi;BIAN Huiyuan;CHENG Qian(School of Geological Engineering and Geomatics,Chang’an University,Xi’an,Shaanxi 710064,China;College of Geo-logy and Environment,Xi’an University of Science and Technology,Xi’an,Shaanxi 710054,China)
出处
《石油地球物理勘探》
北大核心
2025年第4期828-839,共12页
Oil Geophysical Prospecting
基金
国家自然科学基金项目“基于声力效应的页岩动静态弹性特征响应机理研究”(42304143)
陕西省自然科学基础研究计划面上项目“基于体积压裂的页岩气储层可压裂评价方法研究”(2025JC-YBMS-312)联合资助。
