The movable fluid percentage and movable fluid porosity of rocks are important parameters for evaluating the development potential of petroleum reservoirs, which are usually determined by expensive and time-consuming low-field nuclear magnetic resonance (NMR) experiments combined with centrifugation. In this study, an NMR proxy model based on adaptive ensemble learning was proposed to predict the rock movable fluid indexes efficiently and economically. We established adaptive ensemble learning via an opposite political optimizer (AEL-OPO), which adaptively combines 33 base learners through political optimization to increase the prediction accuracy of the NMR proxy model. To improve the generalization ability of the AEL-OPO, opposition-based learning was introduced to improve the global search speed and stability of the political optimizer. Accessible petrophysical parameters such as rock density, porosity, permeability, average throat radius, and maximum throat radius were used as a training set, a validation set, and a test set. The prediction results show that our new strategy outperforms the other 33 base learners, with R2 (coefficient of determination) values of 84.64% in movable fluid percentage and 74.09% in movable fluid porosity.
Unconventional reservoirs are rich in petroleum resources. Reservoir fluid property identification for these reservoirs is an essential process in unconventional oil reservoir evaluation methods, which is significant for enhancing the reservoir recovery ratio and economic efficiency. However, due to the mutual interference of several factors, identifying the properties of oil and water using traditional reservoir fluid identification methods or a single predictive model for unconventional oil reservoirs is inadequate in accuracy. In this paper, we propose a new ensemble learning model that combines 12 base learners using the multiverse optimizer to improve the accuracy of reservoir fluid identification for unconventional reservoirs. The experimental results show that the overall classification accuracy of the adaptive ensemble learning by opposite multiverse optimizer (AIL-OMO) is 0.85. Compared with six conventional reservoir fluid identification models, AIL-OMO achieved high accuracy on classifying dry layers, oil–water layers, and oil layers, with accuracy rates of 94.33%, 90.46%, and 90.66%. For each model, the identification of the water layer is not accurate enough, which may be due to the classification confusion caused by noise interference in the logging curves of the water layer in unconventional reservoirs.
Automatic modulation classification (AMC) is one of the most important technologies in various communication systems, including drone communications. It can be applied to confirm the legitimacy of access devices, help drone systems better identify and track signals from other communication devices, and prevent drone interference to ensure the safety and reliability of communication. However, the classification performance of previously proposed AMC approaches still needs to be improved. In this study, a dual-stream spatiotemporal fusion neural network (DSSFNN)-based AMC approach is proposed to enhance the classification accuracy for the purpose of aiding drone communication because SDDFNN can effectively mine spatiotemporal features from modulation signals through residual modules, long-short term memory (LSTM) modules, and attention mechanisms. In addition, a novel hybrid data augmentation method based on phase shift and self-perturbation is introduced to further improve performance and avoid overfitting. The experimental results demonstrate that the proposed AMC approach can achieve an average classification accuracy of 63.44%, and the maximum accuracy can reach 95.01% at SNR = 10 dB, which outperforms the previously proposed methods.
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