粗糙集和神经网络相融合的钻井安全评价模型

针对动态性、随机性和不确定性较强的钻井作业现场，开展了安全评价的研究。提出了一种基于粗糙集和BP神经网络对作业现场进行安全评价的方法。首先以粗糙集为基础来构建BP神经网络的前置系统，对采集到的样本数据进行属性约简。其次，根据约简结果以及作业当天的事故情况完成了BP神经网络输入层和输出层的设计，并根据输入层和输出层神经元的个数通过试凑法确定网络隐含层的神经元数量范围，并采用训练样本对不同神经元个数所对应的网络模型进行训练，选择网络误差最低的网络作为所构建的网络模型。最后，选取16 d的测试样本对网络进行验证，将网络的输出同作业现场的实际结果进行比较，有14 d的网络结果与实际结果相符，测试准确率达到了87.5%。

An Evaluation Model of Drilling Safety Based on Combined Rough Set and Neural Network

A safety evaluation study was carried out for a drilling site with strong dynamics, randomness, and uncertainty. A safety evaluation method based on a rough set and a BP neural network is proposed for an operational field. First, the pre-system of the BP neural network is constructed based on the rough set, and a simplification of the attributes of the collected sample data is performed. Second, the input and output layers of the BP neural network are constructed based on the simplification results and the accident scenario on that particular operational day. Furthermore, the number of neurons in the hidden layer of the network is determined through a trial and error method based on the number of neurons in the input and output layers. The training samples are used to train the network models with different number of neurons. The network with the lowest error is selected as the constructed network model. Finally, test samples for 16 days are selected to verify the network. The network results are consistent with the actual results for 14 days, indicating that the test accuracy is 87.5%.