Gas content is one of the main parameters to evaluate whether shale gas can be enriched to obtain high-yield.The higher the gas content,the more favorable for shale gas wells to obtain high-yield.Traditional gas content seismic prediction methods are based on single-attribute,multi-attribute linear fitting or simple neural networks,and have low accuracy.The gas content prediction method is based on deep neural network.Through optimizing seismic attributes,optimizing the solution method and choosing the appropriate number of hidden layers,the number of neurons,and the number of iterations,a prediction model can be established to predict the gas content of shale,thus effectively improving the prediction accuracy of shale gas content and providing support for the geological evaluation of shale gas research areas and the deployment of shale gas horizontal wells.
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