多源数据与地质逻辑融合的深层页岩储层构造裂缝测井智能识别OA北大核心

Structural fractures in deep shale reservoirs exert obvious controlling effects on shale gas enrichment and gas production.Their accurate identification holds important guiding significance for the development,and subsequent exploration,of shale gas.In this paper,based on drilling,logging,well-logging,and production data of shale gas in the Longmaxi Formation in southeastern Sichuan Basin,the response characteristics of multisource data to natural fractures are identified via a combination of machine learning and fracture response information.An intelligent identification model of structural fractures,including their developmental characteristics,is constructed,and a quantitative well-logging evaluation method for assessing the effectiveness of structural fracture networks was developed.The results show that four types of structural fractures have developed in the shale of the Longmaxi Formation,which are dominated by high-angle,vertical,tensile fractures and shear fractures.When the core fracture density exceeds 12 pieces per meter in organic-rich shale and eight pieces per meter in organic-bearing shale,the drilling time and gas logging responses become more pronounced.Cross-plot analysis and Pearson correlation coefficient analysis indicated that the acoustic transit time,neutron porosity,deep-to-shallow laterolog resistivity ratio,compressional waves,shear waves,and Stoneley waves exhibit high sensitivity to the density and dip of structural fractures in the shale reservoir.By optimizing a tabular data,deep learning architecture model called TabNet,we achieved identification accuracies exceeding 93%for identifying the fracture scale and 80%for fracture occurrence within organic-rich shale.Compared with other approaches,the model presented here demonstrates the best performance in identifying high-angle,vertical tensile and shear fractures within the lower part of the first member of the Longmaxi Formation.Considering the fractures'geometric parameters and petrophysical properties,a quantitative evaluation index for fracture network effectiveness was established.It is considered that the better the effectiveness of the fracture network,the higher the enrichment degree of shale gas,and the easier it is to form complex fracture networks after transformation.These results can further promote the fine evaluation of deep shale reservoir deposits and the optimization design of fracturing transformation.