Numerical Simulation of Gas-Liquid Gravity Displacement in Vertical Fractures during Drilling of Carbonate Formations.

In petroleum drilling, carbonate formations characterized by natural fractures can result in troublesome gas-liquid gravity displacement, which refers to the phenomenon that the drilling mud leakage and gas kick are simultaneously triggered. This work focuses on clarifying the mechanism of gas-liquid displacement in vertical fractures during the drilling of carbonate formations and investigating the characteristics of gas-liquid displacement under various conditions. First, the bottom hole pressure allowing for gas-liquid gravity displacement is analyzed, which determines the coexistence condition of leakage and kick in vertical fractures. Then, a theoretical model of gas-liquid displacement flow in a vertical fracture is established. To verify the reliability and accuracy of the model, the results of numerical simulation are compared with those of a visualization experiment. The development process and flow characteristics of gas-liquid displacement in the fracture under different conditions are numerically simulated. The effects of pressure difference, drilling mud property, and fracture geometry on the gas-liquid displacement rate are analyzed. It is found that the drilling mud leakage rate increases with the increase of fracture width, fracture height, and drilling mud density, while it decreases with the increase of pressure difference and fracture length. The gas invasion rate increases with the increase of fracture width, fracture height, and pressure difference, while it decreases with the increase of drilling mud density and fracture length. The equations for leakage rate and gas invasion rate are derived by the response surface method, and the methods for mitigating gas-liquid gravity displacement are discussed. It is expected that the present work provides a better understanding of the gas-liquid gravity displacement in carbonate formations.

Sparse Granger Causality Analysis Model Based on Sensors Correlation for Emotion Recognition Classification in Electroencephalography.

In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct a brain network by calculating the causal relationships between EEG sensors and select the key EEG features. Traditional EEG Granger causality analysis uses the L 2 norm to extract features from the data, and so the results are susceptible to EEG artifacts. Recently, several researchers have proposed Granger causality analysis models based on the least absolute shrinkage and selection operator (LASSO) and the L 1/2 norm to solve this problem. However, the conventional sparse Granger causality analysis model assumes that the connections between each sensor have the same prior probability. This paper shows that if the correlation between the EEG data from each sensor can be added to the Granger causality network as prior knowledge, the EEG feature selection ability and emotional classification ability of the sparse Granger causality model can be enhanced. Based on this idea, we propose a new emotional computing model, named the sparse Granger causality analysis model based on sensor correlation (SC-SGA). SC-SGA integrates the correlation between sensors as prior knowledge into the Granger causality analysis based on the L 1/2 norm framework for feature extraction, and uses L 2 norm logistic regression as the emotional classification algorithm. We report the results of experiments using two real EEG emotion datasets. These results demonstrate that the emotion classification accuracy of the SC-SGA model is better than that of existing models by 2.46-21.81%.

Sparse Logistic Regression With L 1/2 Penalty for Emotion Recognition in Electroencephalography Classification.

Emotion recognition based on electroencephalography (EEG) signals is a current focus in brain-computer interface research. However, the classification of EEG is difficult owing to large amounts of data and high levels of noise. Therefore, it is important to determine how to effectively extract features that include important information. Regularization, one of the effective methods for EEG signal processing, can effectively extract important features from the signal and has potential applications in EEG emotion recognition. Currently, the most popular regularization technique is Lasso (L 1) and Ridge Regression (L 2). In recent years, researchers have proposed many other regularization terms. In theory, L q -type regularization has a lower q value, which means that it can be used to find solutions with better sparsity. L 1/2 regularization is of L q type (0 < q < 1) and has been shown to have many attractive properties. In this work, we studied the L 1/2 penalty in sparse logistic regression for three-classification EEG emotion recognition, and used a coordinate descent algorithm and a univariate semi-threshold operator to implement L 1/2 penalty logistic regression. The experimental results on simulation and real data demonstrate that our proposed method is better than other existing regularization methods. Sparse logistic regression with L 1/2 penalty achieves higher classification accuracy than the conventional L 1, Ridge Regression, and Elastic Net regularization methods, using fewer but more informative EEG signals. This is very important for high-dimensional small-sample EEG data and can help researchers to reduce computational complexity and improve computational accuracy. Therefore, we propose that sparse logistic regression with the L 1/2 penalty is an effective technique for emotion recognition in practical classification problems.