Deep-learning-based acceleration method for automatic history matching of reservoir numerical simulation
History matching is an important technique to reduce geological uncertainty in reservoir modeling,and it is the ba-sis for oil field production prediction and development scheme design.Since a reservoir geo-model contains a lot of parame-ters with thousands or even millions of uncertain data,a repeated invocation of the reservoir numerical simulator tremendously impacts the computational efficiency during history matching.To solve this problem,a multi-view deep convolution coding-decoding neural network model was proposed for surrogate reservoir modeling.The model consists of a coding-decoding unit and a time-processing unit.The coding-decoding unit embedded in the variety of view networks(VoVNet)can extract the spatial features of the input parameters,while the time processing unit was used to capture the influence of time series.The trained surrogate model can predict pressure and saturation from permeability data in an image-to-image form,providing fast production performance prediction for automatic history matching.Moreover,the proposed surrogate model was incorporated into a multiple data assimilation ensemble smoother(ES-MDA)framework to create a fast deep-learning-based automatic his-tory matching method.The results show that the proposed surrogate model can effectively predict the pressure and saturation distributions in the reservoir at a given time.The production performance predicted using the surrogate model is consistent with that calculated using the traditional reservoir simulation models,while the calculation efficiency is improved extensively.The surrogate-based automatic history matching method can provide accurate inversion of the permeability distribution and demonstrated superiorities in computational efficiency.
automatic history matchingnumerical reservoir simulationsurrogate modeldeep learning
万方数据
维普
