Fractures are critical to subsurface activities such as oil and gas extraction,geothermal energy production,and carbon storage.Hydraulic fracturing,a technique that enhances fluid production,creates complex fracture n...Fractures are critical to subsurface activities such as oil and gas extraction,geothermal energy production,and carbon storage.Hydraulic fracturing,a technique that enhances fluid production,creates complex fracture networks within rock formations containing natural discontinuities.Accurately distinguishing between hydraulically induced fractures and pre-existing discontinuities is essential for understanding hydraulic fracture mechanisms.However,this remains challenging due to the interconnected nature of fractures in three-dimensional(3D)space.Manual segmentation,while adaptive,is both labor-intensive and subjective,making it impractical for large-scale 3D datasets.This study introduces a deep learning-based progressive cross-sectional segmentation method to automate the classification of 3D fracture volumes.The proposed method was applied to a 3D hydraulic fracture network in a Montney cube sample,successfully segmenting natural fractures,parted bedding planes,and hydraulic fractures with minimal user intervention.The automated approach achieves a 99.6%reduction in manual image processing workload while maintaining high segmentation accuracy,with test accuracy exceeding 98%and F1-score over 84%.This approach generalizes well to Brazilian disc samples with different fracture patterns,achieving consistently high accuracy in distinguishing between bedding and non-bedding fractures.This automated fracture segmentation method offers an effective tool for enhanced quantitative characterization of fracture networks,which would contribute to a deeper understanding of hydraulic fracturing processes.展开更多
基金supported through the Natural Sciences and Engineering Research Council of Canada(NSERC)Discovery Grants 341275,CRDPJ 543894-19NSERC/Energi Simulation Industrial Research Chair program.
文摘Fractures are critical to subsurface activities such as oil and gas extraction,geothermal energy production,and carbon storage.Hydraulic fracturing,a technique that enhances fluid production,creates complex fracture networks within rock formations containing natural discontinuities.Accurately distinguishing between hydraulically induced fractures and pre-existing discontinuities is essential for understanding hydraulic fracture mechanisms.However,this remains challenging due to the interconnected nature of fractures in three-dimensional(3D)space.Manual segmentation,while adaptive,is both labor-intensive and subjective,making it impractical for large-scale 3D datasets.This study introduces a deep learning-based progressive cross-sectional segmentation method to automate the classification of 3D fracture volumes.The proposed method was applied to a 3D hydraulic fracture network in a Montney cube sample,successfully segmenting natural fractures,parted bedding planes,and hydraulic fractures with minimal user intervention.The automated approach achieves a 99.6%reduction in manual image processing workload while maintaining high segmentation accuracy,with test accuracy exceeding 98%and F1-score over 84%.This approach generalizes well to Brazilian disc samples with different fracture patterns,achieving consistently high accuracy in distinguishing between bedding and non-bedding fractures.This automated fracture segmentation method offers an effective tool for enhanced quantitative characterization of fracture networks,which would contribute to a deeper understanding of hydraulic fracturing processes.
