Submersible pumpset failure prediction using artificial intelligence methods

It is well-known that large amounts of data are collected and processed during the operation of electric submersible pumps. To optimize the work of mining control center operators, it is recommended to use an automated emergency prevention system. In this way, operators will be able to receive timely information about possible equipment failures, which in its turn will increase the service life of the equipment and reduce operating costs for repairs. The purpose of the present research is to develop a model predicting submersible pumping equipment failures using the method of artificial intelligence. To identify the most accurate model, the paper compares the following forecasting methods: the nearest neighbour method and the linear classifier building method. The presented correlation was created on the basis of 30 parameters obtained from 272 wells of the Eastern Siberia field. Being used, it enabled error-free prediction of failures and complications in pumping equipment operation depending on the gas factor and frequency. Thus, the developed model can be used by oil and gas enterprises to predict failures and accidents in the operation of submersible pumping equipment. The conducted study shows that the prediction accuracy of the required parameter in the developed artificial intelligence model exceeds the results of conventional statistical methods. The model also can be useful for future optimization of processes when field planning and developing. Artificial intelligence is the best prediction method of submersible pumping equipment failures, due to its high speed and accuracy, cognitive technologies are widely used in big data processing.

submersible pumpset, artificial intelligence, prediction, correlation coefficient, mean absolute error