Estimation of gas hydrate saturation based on seismic attributes and deep feedforward neural network
Gas hydrate saturation is an important parameter to evaluate the potential of gas hydrate resources.Conventional seismic inversion methods for reservoir prediction,however,exhibit limitations in accuracy and efficiency,mainly due to their inability to adequately address the inherent nonlinear relationship between seismic data and reservoir parameters.The rapid development of artificial intelligence(Al)has led to the exploration of novel approaches for seismic exploration.Among these,artificial neural networks(ANNs)have emerged as a promising tool.ANNs possess the capability to learn complex patterns from large datasets,enabling them to approximate nonlinear functions with high accuracy.This inherent ability to model complex relationships makes ANNs particularly suitable for inverting subsurface reservoir characteristics,including gas hydrate saturation.Therefore,this paper proposes a novel method for predicting gas hydrate saturation based on seismic attributes and deep feedforward neural networks.This approach consider the advantages and disadvantages of conventional linear formula and petrophysical model methods.The proposed method involves a three-step process.First,different types of seismic attribute bodies with a strong correlation to gas hydrate saturation are identified based on logging and seismic data.These attributes are then used to construct multi-dimensional sample label data.Second,a strategy combining seismic inversion with end-to-end(seismic data-reservoir physical data)inversion is used to test and train the parameters of the fully connected neural network.This includes optimizing the number of hidden layers,the number of neurons,and the number of iterations.Finally,the trained neural network is applied to the seismic data body to obtain predictions of hydrate saturation.Actual data application results show that the saturation predictions based on seismic attributes and deep feedforward neural networks exhibit high accuracy and low multi-solution,showing good agreement with the logging data,which proves that the method has good application value.Furthermore,the predicted spatial distribution characteristics of hydrate indicate that the hydrate accumulation pattern in the study area is a flat-lying transitional free gas to gas hydrate system.
gas hydratesdeep learningsaturationseismic attributesdeep feedforward neural networks
万方数据
维普
