GANSim is a generative adversarial networks(GANs)-based geomodelling framework with direct conditioning capabilities.To extend GANSim for geomodelling of multi-scenario and non-stationary reservoirs,and to address its...GANSim is a generative adversarial networks(GANs)-based geomodelling framework with direct conditioning capabilities.To extend GANSim for geomodelling of multi-scenario and non-stationary reservoirs,and to address its tendency to overlook single-pixel well facies conditioning data that can cause local facies disconnections around wells,an enhanced GANSim framework is proposed.The effectiveness of the enhanced GANSim is validated using a 3D multi-scenario,non-stationary turbidite fan reservoir.For reservoirs that may involve multiple geological scenarios,two GANSim geomodelling workflows are proposed:(1)training a comprehensive GANSim model that covers all possible geological scenarios;and(2)first performing geological scenario falsification and then training GANSim models only for the unfalsified scenarios.On this basis,a local discriminator architecture is designed to improve facies continuity around wells.The modelling results show that both workflows can generate non-stationary facies models that conform to expected geological patterns and honor conditioning data,and the facies discontinuity issue around wells is effectively resolved.Compared with multipoint geostatistical methods(SNESIM),GANSim exhibits superior capability in reproducing geological patterns and modelling efficiency.Although GANSim requires a long training time,once training is completed,it can be applied to geomodelling reservoirs of arbitrary scale with similar geological structures,achieving modelling speeds approximately 1000 times faster than SNESIM.展开更多
文摘GANSim is a generative adversarial networks(GANs)-based geomodelling framework with direct conditioning capabilities.To extend GANSim for geomodelling of multi-scenario and non-stationary reservoirs,and to address its tendency to overlook single-pixel well facies conditioning data that can cause local facies disconnections around wells,an enhanced GANSim framework is proposed.The effectiveness of the enhanced GANSim is validated using a 3D multi-scenario,non-stationary turbidite fan reservoir.For reservoirs that may involve multiple geological scenarios,two GANSim geomodelling workflows are proposed:(1)training a comprehensive GANSim model that covers all possible geological scenarios;and(2)first performing geological scenario falsification and then training GANSim models only for the unfalsified scenarios.On this basis,a local discriminator architecture is designed to improve facies continuity around wells.The modelling results show that both workflows can generate non-stationary facies models that conform to expected geological patterns and honor conditioning data,and the facies discontinuity issue around wells is effectively resolved.Compared with multipoint geostatistical methods(SNESIM),GANSim exhibits superior capability in reproducing geological patterns and modelling efficiency.Although GANSim requires a long training time,once training is completed,it can be applied to geomodelling reservoirs of arbitrary scale with similar geological structures,achieving modelling speeds approximately 1000 times faster than SNESIM.
