Reservoir Porosity Prediction Method Based on SSA-BiGRU
GAO Yatian
WANG Ran
WU Runtong
Abstract：It is very important for reservoir evaluation to predict reservoir porosity by logging data.Aiming at the problem that the existing porosity prediction model cannot deeply explore the potential relationship between logging data and porosity,this paper proposes a reservoir porosity prediction model based on the sparrows search algorithm(SSA)to optimize bidirectional gated recur-rent neural network(BiGRU),which takes logging data as input.BiGRU is used to explore the nonlinearity and time series charac-teristics between logging curves and porosity.The sparrow search algorithm is used to optimize the parameters of BiGRU neural net-work model,such as the number of neurons in each layer,batch size and learning rate,and get the optimal parameter values.The problem of low prediction accuracy caused by empirical selection or manual parameter adjustment is overcome.The experimental re-sults show that the SSA-BiGRU prediction model effectively improves the prediction accuracy compared with BP,LSTM,BiGRU and other porosity prediction models.
Keywords：porosity predictionbidirectional gated recurrent unit neural networksparrow search algorithmprediction ac-curacy
Publication Date：2025-06-20
Online Publishing Date：2025-09-23(First online date of this platform, not the publication date of the document)
Pages：6( 1613-1618 )