A new approach for porosity and permeability prediction from well logs using artificial neural network and curve fitting techniques: A case study of Niger Delta, Nigeria

A new approach based on artificial neural network and standardized non-linear regression analysis is proposed for the prediction of reservoir properties from limited well log data. Two separate multi-layer-perceptron neural networks using the Bayesian framework were employed and mathematical models were developed from the networks. These neural networks were used to predict reservoir properties from well log data. The porosity neural network (NN) utilized density log (RHOB) as input and core porosity as target, while the permeability NN employed RHOB and water saturation as input and core derived permeability as target. Mean square errors (MSEs) for training, validation and test for the porosity network were 1.20889e(-6). 2.22574e(-6), and 1.37204e(-6) respectively while 5.62849e(-1) ,3.13709e(-1), and 4.83728e(-1) were obtained for the permeability network. These networks were later applied to four wells to predict porosity and permeability. Very good correlation coefficients (R-values) between the NN predicted data and the target data were obtained in each of these wells. For the porosity network R-values of 0.99987, 0.99099, 0.99474 and 0.83749 were obtained, whereas 0.97584, 0.83594, 0.97002 and 0.83512 respectively were observed from the permeability network. For the model development, standardized nonlinear regression curve fitting tool was utilized. The polynomial model that determines the coefficients of the fitted model through the Gauss-Newton optimization method was used. Several criteria such as robust fitting, normalization and scaling were employed to further reduce and randomize the errors so that it follows a Gaussian distribution with zero mean and constant variance. Goodness of the fit was tested with the observable statistical parameters. For the porosity model, the sum of squared error (SSE) of 2.2715 e(-04). R-square of 0.9997, Adjusted R-square of 0.9997 and root mean square error (RSME) of 4.3562 e(-04) was observed while the permeability model, SSE of 7.8315e(01), R-square of 0.9753, Adjusted R-square of 0.9752 and RSME of 8.092 was observed. This approach provides a simple and cost effective way of estimating reservoir properties. (C) 2020 Elsevier B.V. All rights reserved.