Logging curve reconstructions based on MLP multilayer perceptive neural network

As an important component of the petroleum industry, logging curves play an important role in lithology identification, reservoir evaluation, and geological structure analysis. However, instrument measurement or drilling problems often result in distorted or discontinuous logging curves in certain profiles. Retesting is costly and challenging to operate. Traditional linear fitting and statistical analysis are insufficient for the analysis and evaluation of ultrafine reservoirs. To address this issue, MLP neural network (multi-layer perception) technology has established a curve prediction model based on training data. The nonlinear factors are introduced through the activation function, the optimal solution is found by using the optimisation methods of loss function and MBGD (small batch gradient descent method), and the best neural network model is obtained by optimising the number of hidden layers and nodes of the neural network. Compare the predicted curve with the original measurement curve for quality control to obtain the best result in constructing curve weights. The results show that MLP neural network technology can provide reliable technical support for accurately predicting logging curves and enhancing reservoir analysis and evaluation. [Received: March 8, 2023; Accepted: May 5, 2023]