Abstract：Formation parameters such as lithology,resistivity,porosity,permeability and saturation can be obtained by logging interpretation.However,it is frequently the case that logging data are missing or incomplete,but it is difficulty and expensive to record again.Present reconstruction of logging data based on traditional linear hypothesis and statistical analysis can not meet the requirements of fine description of reservoir characteristics.Gated Recurrent Unit (GRU) neural network is a new deep learning algorithm which is suitable for solving nonlinear and sequential problems.Based on the latest achievements of deep learning,a logging reconstruction method based on GRU neural network is proposed.The method considers the nonlinear mapping between logging data,how logging data change with depth and the relationship among historical data.Tested with real logging data and compared with the multiple regression method,the GRU network model is effective to reconstruct logging traces.It is a new idea a for logging traces reconstruction.
王俊, 曹俊兴, 尤加春. 基于GRU神经网络的测井曲线重构[J]. 石油地球物理勘探, 2020, 55(3): 510-520.
WANG Jun, CAO Junxing, YOU Jiachun. Reconstruction of logging traces based on GRU neural network. Oil Geophysical Prospecting, 2020, 55(3): 510-520.
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