This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data.The NMR l...This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data.The NMR logging data have some highly vital privileges over conventional ones.The measured porosity is independent from bearer pore fluid and is effective porosity not total.Moreover,the permeability achieved by exact measurement and calculation considering clay content and pore fluid type.Therefore availability of the NMR data brings a great leverage in understanding the reservoir properties and also perfectly modelling the reservoir.Therefore,achieving NMR logging data by a model fed by a far inferior and less costly conventional logging data is a great privilege.The input parameters of model were neutron porosity(NPHI),sonic transit time(DT),bulk density(RHOB)and electrical resistivity(RT).The outputs of model were also permeability and porosity values.The structure developed model was build and trained by using train data.Graphical and statistical validation of results showed that the developed model is effective in prediction of field NMR log data.Outcomes show great possibility of using conventional logging data be used in order to reach the precious NMR logging data without any unnecessary costly tests for a reservoir.Moreover,the considerable accuracy of newly ANN-Cuckoo method also demonstrated.This study can be an illuminator in areas of reservoir engineering and modelling studies were presence of accurate data must be essential.展开更多
The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and co...The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.展开更多
文摘This work highlights the application of Artificial Neural Networks optimized by Cuckoo optimization algorithm for predictions of NMR log parameters including porosity and permeability by using field log data.The NMR logging data have some highly vital privileges over conventional ones.The measured porosity is independent from bearer pore fluid and is effective porosity not total.Moreover,the permeability achieved by exact measurement and calculation considering clay content and pore fluid type.Therefore availability of the NMR data brings a great leverage in understanding the reservoir properties and also perfectly modelling the reservoir.Therefore,achieving NMR logging data by a model fed by a far inferior and less costly conventional logging data is a great privilege.The input parameters of model were neutron porosity(NPHI),sonic transit time(DT),bulk density(RHOB)and electrical resistivity(RT).The outputs of model were also permeability and porosity values.The structure developed model was build and trained by using train data.Graphical and statistical validation of results showed that the developed model is effective in prediction of field NMR log data.Outcomes show great possibility of using conventional logging data be used in order to reach the precious NMR logging data without any unnecessary costly tests for a reservoir.Moreover,the considerable accuracy of newly ANN-Cuckoo method also demonstrated.This study can be an illuminator in areas of reservoir engineering and modelling studies were presence of accurate data must be essential.
基金supported by the National Natural Science Foundation of China(51875465)
文摘The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.
