###### Abstract  
In order to improve the measurement speed and prediction accuracy of unconventional reservoir parameters, the deep neural network (DNN) is used to predict movable fluid percentage of unconventional reservoirs. The Adam optimizer is used in the DNN model to ensure the stability and accuracy of the model in the gradient descent process, and the prediction effect is compared with the back propagation neural network (BPNN), K-nearest neighbor (KNN), and support vector regression model (SVR). During network training, L\({}_{2}\) regularization is used to avoid over-fitting and improve the generalization ability of the model. Taking nuclear magnetic resonance (NMR) T\({}_{2}\) spectrum data of laboratory unconventional core as input features, the influence of model hyperparameters on the prediction accuracy of reservoir movable fluids is also experimentally analyzed. Experimental results show that, compared with BPNN, KNN, and SVR, the deep neural network model has a better prediction effect on movable fluid percentage of unconventional reservoirs; when the model depth is five layers, the prediction accuracy of movable fluid percentage reaches the highest value, the predicted value of the DNN model is in high agreement with the laboratory measured value. Therefore, the movable fluid percentage prediction model of unconventional oil reservoirs based on the deep neural network model can provide certain guidance for the intelligent development of the laboratory's reservoir parameter measurement.  
deep learning; NMR T\({}_{2}\) spectrum; movable fluid percentage; deep neural network; unconventional reservoir +
Footnote †: journal: Medical  
fluid percentage of the reservoir is the core nuclear magnetic resonance technology [3]. The current disadvantages of this technology are the long experimental period and the large amount of manpower required, which make it difficult to synchronize the evaluation of the reservoir with the development and deployment of the oil field.  
In recent years, with the development of science and technology, artificial intelligence methods have been increasingly applied to the petroleum industry: [PERSON] and [PERSON] used the XGBoost algorithm to identify complex carbonate rock lithology with an accuracy rate of 88.18% [4; 5]. [PERSON] used machine learning methods to study lithology classification, and concluded that the classification accuracy of supervised learning algorithms was better than that of unsupervised algorithms [6]; [PERSON] predicted the porosity of sandstone reservoirs based on deep neural network and logging data. The correlation between the predicted value of the model and the actual porosity is as high as 0.9725 [7]. [PERSON] combined the NMR transverse relaxation time spectrum and mercury intrusion data to intelligently predict the permeability of sandstone reservoirs through BP neural network and achieved a good prediction accuracy [8]; [PERSON] realized the automatic generation of logging curves based on cyclic neural network [9]. [PERSON] predicted unconventional reservoir saturation based on NMR logging data and machine learning methods [10]. Deep learning was first proposed by [PERSON] in 2006. The ability to achieve complex nonlinear fitting through artificial neural networks with multiple hidden layers had greatly improved the prediction and classification accuracy of artificial intelligence models [11]. NMR logging data are affected by many factors in the reservoir, and the effective information contained in NMR logging often has large deviations. In order to improve measuring speed and accuracy of the movable fluid percentage of unconventional reservoirs, this article is based on the deep learning method and the unconventional reservoir core NMR T\({}_{2}\) spectrum data of laboratory measurement to predict movable fluid percentage of unconventional oil reservoirs.