As a biometric characteristic, electroencephalography (EEG) signals have the advantages of being hard to steal and easy to detect liveness, which attract researchers to study EEG-based personal identification technique. Among different EEG protocols, resting state signals are the most practical option since it is more convenient to operate than the other protocols. In this paper, a personal identification system based on resting state EEG is proposed, in which data augmentation and convolutional neural network are combined. The cross-validation is performed on a public database of 109 subjects. The experimental results show that when only 14 EEG channels and 0.5 seconds data are employed, the average accuracy and average equal error rate of the system can reach 99.32% and 0.18%, respectively. Compared with some existing representative works, the proposed system has the advantages of short acquisition time, low computational complexity, and rapid deployment using market available low-cost EEG sensors, which further advances the implementation of practical EEG-based identification systems.

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Mud loss is one of the most common and troublesome wellbore problems. Predictive evaluation of mud loss types not only optimizes drilling design, but also reduces potential costs before drilling. To solve mud loss problem of M formation in the H oil field, we proposed a practical solution based on machine learning in this paper, which can predict the mud loss types using seismic data. Firstly, we calculated and obtained 16 seismic attributes in 6 categories, and according to the mud loss rate and volume, we classified the mud loss into four types: seepage loss, partial loss, severe loss, and total loss. Then 10 characteristics wells were selected from 50 wells, which covered different mud loss types and depth. The seismic attributes of single well with the above characteristics were extracted, and the relationship between seismic attributes and mud loss type were obtained using machine learning. Finally, a 3D probability prediction model of potential mud loss type is obtained and analyzed with a practical case. Our model can predict the distribution of mud loss types at different depths in different regions. It can not only be used in the design of well location and well trajectory but also provide scientific suggestions for mud loss prevention and plugging.

The rate of penetration (ROP) is directly related to the drilling cost. Accurate prediction of the ROP is of great significance to reduce the drilling cost. At present, there are two main types of methods for predicting ROP. One is based on physical mechanism analysis. However, due to many kinds of parameters affecting ROP, the prediction results are often very different from the actual ROP. The other is the data-driven prediction model of ROP. For the sample data with no obvious characteristics or "small sample" data, compared with the actual results, the prediction results of the data-driven model have large errors. To solve the above problems, a new machine learning ROP prediction model based on physical mechanism constraints is proposed in this paper. Based on the data of 4 wells in X block of T oil field, the random forest model is selected from the three machine models including support vector regression, random forest, and regression tree, and the rock drillability constraint is added to predict the ROP. The prediction results that do not meet the rock drillability constraints are optimized to reduce the prediction error. The results show that the random forest model with constraints has the best performance, and the prediction error is reduced from 14.52% to 11.27%.

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