Objective The aim is to solve the problem that the contamination type of the completion fluid can not be effectively identified after being contained by brine and residual acid during the drilling of the target layer.

Methods The contamination of brine and residual acid with different mass fractions of the completion fluid was measured, and the labels of the data samples with different contamination degrees were revised by K-means clustering algorithm. Different BP neural network models were trained according to the difficulty of obtaining data sample features and the number of hidden layers, and the classification accuracy of the models was tested by leave-one-out cross validation method.

Results It is found that the more features the data samples possess, the higher classification accuracy of the trained BP neural network could be achieved, while more hidden layers would lower the classification accuracy. The BP neural network model with one hidden layer was subsequently established with data samples that contain four kinds of features including "rheology+aging+filtration loss+well name". The average classification accuracy rate reached as high as 93.18%.

Conclusions The BP neural network model trained by rheology and filtration loss features can be quickly deployed in the oil-testing sites to solve the problem of failing to identify the type of completion fluid contamination due to the lack of special equipment in the field.