The accuracy of traditional reconstruction methods is often insufficient to solve the problem of data missing or distortion on acoustic log curves.Deep learning has a strong ability of data characterization,but model building suffers from hyperparameter uncertainties and time cost.To solve these problems,the asynchronous successive halving algorithm(ASHA)is combined with the long short-term memory neural network(LSTM)to formulate hyperparameter optimized LSTM for data reconstruction.A case study in Daqing Oilfield involves 6 wells.Through a correlation analysis,natural gamma ray,density and compensated neutron are selected as input characteristic parameters to build the LSTM learning model,for which hyperparameter optimization is performed using the ASHA.In view of efficiency and accuracy,we compare ASHA optimization with Bayesian optimization and particle swarm optimization.The portfolio of optimized hyperparameters is finally applied to the LSTM model.Compared with multiple re-gression,GRU and BILSTM models,the ASHA can determine model hyperparameters with improved efficiency and accuracy and less time and labor costs.The ASHA optimized LSTM model could reconstruct acoustic log curves with high accuracy.
well logging curve generationdeep learningASH ALSTMhyperparameter optimization
维普
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