Well Logs Reconstruction Based on Deep Learning Technology

Well logs play a very important role in formation evaluation, but due to the influence of geological, engineering, and other factors in practical applications, some logs are often distorted or missing. The log curve reconstruction methods based on traditional empirical model and statistical analysis are unconvincing enough, so it is proposed to reconstruct the log curves by deep learning method. Considering the limitation of neural network, long short-term memory (LSTM) neural network is studied, and we utilize the formation lithology index (FLI) of logging domain knowledge (DK) to filter data. As a result, high-quality training sample data are acquired, which are used as the basis for deep learning to reconstruct log curves. Meanwhile, the LSTM model with DK constraint layer is constructed and trained. Then, particle swarm optimization (PSO) algorithm is chosen to optimize the LSTM hyperparameters—initial learning rate and dropout probability. Finally, a log curve reconstruction model (PSO-DK-LSTM) combining DK and PSO algorithm is created. We design one experiment to verify prediction capability of PSO-DK-LSTM. The whole data are collected from a work area of XX oilfield. The verification results show that the log reconstruction method based on PSO-DK-LSTM has achieved better results in terms of accuracy and stability, which gives a new idea for log reconstruction.