Prediction of reservoir key parameters in ‘sweet spot’ on the basis of particle swarm optimization to TCN-LSTM network

Abstract

In oil reservoirs, the sweet spot is found that the well could be positioned quickly and accurately, the drilling rate and the oil-gas production are increased, development cost is reduced. Among them, sorting, granularity and porosity are important factors to evaluate whether the exploration area is a sweet spot. A low permeability oil reservoir is taken as the research object, this paper mainly focuses on the prediction of the above evaluation parameters. In order to solve this problem, this paper proposed a new deep learning hybrid model. The model is constructed based on temporal convolutional network (TCN) and long short-term memory network (LSTM). Firstly, the influence of logging parameters on the prediction of evaluation indexes is analyzed. Secondly, the model is used to predict the screened sequence data. In the process of model construction, particle swarm optimization (PSO) is used to optimize the global hyperparameters, and finally the prediction model is obtained. The model is compared with TCN algorithm, traditional machine learning and empirical formula. This model improves the prediction accuracy of reservoir evaluation parameters in low permeability oilfield.