Shale　gas　plays　a　crucial　role　as　an　exploration　resource　for　human　life,　and　its　contribution　to　energy　production　has　been　steadily　increasing.　The　success　of　hydraulic　fracturing　operations　is　highly　dependent　on　the　understanding　of　shale　fracture　propagation　at　large　scales.　Currently,　the　segmentation　of　large-scale　shale　fractures　is　primarily　carried　out　through　time-consuming　manual　or　semi-automated　methods.　The　identification　of　shale　fractures　is　similar　to　image　segmentation,　in　which　deep-learning　holds　the　potential　to　achieve　accurate　performance　close　to　human　experts.　However,　its　application　encounters　challenges　when　dealing　with　large-scale　shale　images　due　to　factors　such　as　a　lack　of　datasets,　severe　artifacts,　and　limited　sample　sizes.　In　this　study,　we　compiled　the　first　publicly　available　large-scale　shale　CT　dataset,　which　consists　of　a　total　of　4313　scanned　images,　with　over　700　images　coming　from　scanning　experiments　on　300　cm3　cubic　shale　samples.　We　also　proposed　a　new　data　augmentation　method　for　shale　CT　images　to　overcome　challenges　posed　by　severe　artifacts　and　small-sample　sizes.　This　method　involves　simulating　image　artifacts　and　reducing　high　edge　gradients　along　the　sample　edges,　which　has　proven　to　be　effective　in　facilitating　the　efficient　training　of　deep-learning　models.　We＇ve　also　conducted　several　ablation　studies　to　determine　the　best　model　design,　namely　ShaleSeg.　ShaleSeg　was　able　to　accurately　identify　multi-scale　fractures,　achieving　a　precision　of　0.84　and　an　F-score　of　0.81,　which　is　close　to　the　manual　results.　In　the　case　of　minor　fractures,　precision　increased　by　an　increase　significantly　from　0.191　to　0.513,　compared　to　the　traditional　algorithm.　ShaleSeg　significantly　improves　the　accuracy　and　efficiency　of　fracture　segmentation　for　large-scale　shale　images.　This　advancement　will　be　crucial　for　a　thorough　comprehension　of　the　fracture　mechanism　in　shale.