Author name cluster

Shurui Fan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

EAAI Journal 2023 Journal Article

Oil Logging Reservoir Recognition Based on TCN and SA-BiLSTM Deep Learning Method

Wenbiao Yang
Kewen Xia
Shurui Fan

The use of Deep Learning methods to mine useful and critical information from massive and complex logging datasets is of great importance for oil logging reservoir recognition. TCN-SA-BiLSTM was proposed due to the lack of previous studies to mine the internal correlation of the features of the logging dataset. TCN-SA-BiLSTM is a deep learning model that hybridizes Temporal Convolutional Network (TCN), Self-Attention mechanism (SA), and Bidirectional Long Short Term Memory network (BiLSTM). First, for the pre-processed feature data, TCN is used for feature extraction with parallel convolution operation. Then, by exploiting the ability of SA to extract the internal autocorrelation of time series features, this can better capture the dependence of feature data over long distances. Finally, the contextual linkage of the features is further obtained using BiLSTM. The experimental results show that TCN-SA-BiLSTM exhibits excellent performance in comparison with seven competing models on all performance evaluation metrics. It overcomes the deficiencies in capability exhibited by traditional logging interpretation techniques to improve the efficiency and success rate of oil and gas exploration.

Details DOI

EAAI Journal 2023 Journal Article

Self-Attention Causal Dilated Convolutional Neural Network for Multivariate Time Series Classification and Its Application

Wenbiao Yang
Kewen Xia
Zhaocheng Wang
Shurui Fan
Ling Li

Time Series Classification (TSC) in data mining is gradually developing as an important research direction. Many researchers have developed an extensive interest in Multivariate Time Series Classification (MTSC). The Self-Attention Causal Dilated Convolutional Neural Network (SACDCNN) is proposed to address the limitations of existing models that perform poorly on classification tasks. It designs the residual and dense blocks based on Causal Dilated Convolution based on the traditional residual and dense networks that still have superior performance after deepening the network hierarchy and the dependence of time series on long-range information. A Self-Attention mechanism (SA) is also incorporated to extract the internal autocorrelation of time series features. Comparison experiments on 20 benchmark University of California, Riverside (UCR) and University of California, Irvine (UCI) datasets with eight high-performance classification models show that the method can improve the classification accuracy of time series datasets. Finally, it was applied to petroleum logging reservoir recognition, and a comparison experiment was conducted on two wells. The results show that SACDCNN is effective and significantly superior. It overcomes the shortcomings of traditional logging interpretation techniques and improves the efficiency and success rate of oil and gas exploration.

Details DOI

EAAI Journal 2022 Journal Article

A Multi-Strategy Whale Optimization Algorithm and Its Application

Wenbiao Yang
Kewen Xia
Shurui Fan
Li Wang
Tiejun Li
Jiangnan Zhang
Yu Feng

Whale Optimization Algorithm (WOA) is a key tool for solving complex engineering optimization problems, aiming at adjusting important parameters to satisfy constraints and optimal objectives. WOA has a simple structure, few parameters, high search capability, and easy implementation. However, it suffers from the same problems as other metaheuristic algorithms of being prone to local optima and slow convergence, for which the Multi-Strategy Whale Optimization Algorithm (MSWOA) is proposed. Four strategies are introduced in MSWOA. Firstly, a highly randomized chaotic logistic map is used to generate a high-quality initial population. Secondly, exploitation and exploration are enhanced by setting adaptive weights and dynamic convergence factors. Further, a Lévy flight mechanism is introduced to maintain the population diversity in each iteration. Finally, the Evolutionary Population Dynamics (EPD) mechanism is introduced to improve the efficiency of search agents in finding the optimum. Another problem lies in the Semi-Supervised Extreme Learning Machine (SSELM) based on manifold regularization is an effective classification and regression model, but the random generation of input weights and hidden layer thresholds and the grid selection of hyperparameters lead to unsatisfactory classification performance. To this end, we developed the MSWOA-SSELM model, optimally selected the parameters of SSLEM using MSWOA, and applied it to logging layer recognition, which effectively improved the accuracy of logging interpretation. By comparing the experiments with 14 swarm intelligence algorithms on 18 benchmark test functions, the CEC2017 benchmark suite, and an engineering application problem, the experimental results show that MSWOA is significantly superior and effective in solving global optimization problems. Finally, the proposed MSWOA-SSELM is applied in three wells and outperforms other classification models in terms of Accuracy (ACC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). It obtained the best results with 96. 2567% ACC, MAE of 0. 0749, and RMSE of 0. 3870.

Details DOI