Fracture prediction is an important and active area of research for oil and gas exploration in fractured unconventional reservoirs. Traditionally, fracture prediction techniques come in two flavors, pre-stack anisotropy-based or post-stack edge-enhancement attributes such as ant-tracking and maximum likelihood. Inaccurate predictions may result from apparent low signal-to-noise ratio in the pre-stack domain approaches, or from cumulative effects that are misrepresented in post-stack data; there are also shortcomings from using a single pre- or post-stack domain. We propose a comprehensive multi-scale prediction framework to delineate: major faults (using Deep Learning), associated minor faults (using seismic gradient disorder), and fractures (using seismic aberrance). The principles of Deep Learning, seismic gradient disorder and aberrance are introduced and their application effects are verified through the study of tight sandstone reservoir fractures in the Hutubi area, southern margin of Junggar Basin. Keywords: Multi-scale fracture, Deep learning, Seismic gradient disorder, Aberrance, Stress analysis
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