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

doi:10.11885/j.issn.16745086.2016.01.19.02

Abstract

Abstract: 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%.

Key words: safety evaluation, rough set, BP neural network, attribute simplification, drilling operation

CLC Number:

TE28

LI Jian, LI Ke, WANG Bing. An Evaluation Model of Drilling Safety Based on Combined Rough Set and Neural Network[J]. 西南石油大学学报(自然科学版), 2017, 39(5): 120-128.

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