Building a Geologic Model of a Meandering River Using Conditional Generative Adversarial Networks
[Objective]In traditional channel modeling methods,object-based methods are difficult to characterize meandering river point bars,and it is difficult to condition.Multi-point geostatistics makes it difficult to simulate the continuous morphology of channel.Conditional Generative Adversarial Networks can generate complex graphics that meet certain conditions,which can solve the difficulties in characterizing point bars and channel morphology during the establishment of geological models for meandering rivers.Moreover,the generated models can meet the given well point conditions.[Methods]Taking a gas field in the southern part of the Sulige gas field in the Ordos Basin as an ex-ample,a three-dimensional modeling method for meandering rivers based on conditional generative adversarial net-works was studied.In the modeling process,firstly,200 meandering river models were established using the Allu-vsim modeling method based on the characteristics of the meandering rivers in the work area.Then,deep learning was performed on 200 models using convolutional neural networks to extract the feature matrix of the models.A gener-ator capable of generating meandering river models was established using Conditional Generative Adversarial Net-works.Finally,taking the well data of the work area as input data,a 3D model that satisfies the complex shape of the well data and the meandering river is established using a generator.[Results and Conclusions]The results indicate that the established model can well demonstrate the three-dimensional morphology and corresponding relationship be-tween the channel and point bar in meandering rivers.To clarify the key factors that affect the model results,it was found through comparing the training times with the input data that an appropriate training times(160 times)and a large number of input samples(200 samples)are prerequisites for establishing a model that meets the working area conditions.In addition,by comparing traditional geological modeling methods,the conditional generative adversarial network modeling method can effectively reproduce the spatial morphology of channel sediment bodies,overcome the shortcomings of traditional meandering river modeling methods in terms of conditioning difficulties,and provide a new solution for modeling channel sand bodies in meandering river sedimentary environments.The established mean-dering river model can provide reference for the oilfield development stage.
万方数据
维普
