Application of Artificial Neural Network for Prediction of Screw Downhole Motor Performance

The article deals with issues related to the solution of problems of diagnostics of technical condition of drilling equipment and forecasting its performance. The possibility of building neural networks as a tool for practical solution of applied problems in the field of diagnosing and forecasting the performance of technological equipment is considered. Developed a digital layout of the screw downhole motor (SDM), this is what used to be called a "set of design and technological documentation" that is the basis for the implementation of the final product in the oil and gas complex (COG) on the example of SDM. The article presents the results of the use of digital technologies that will improve the accuracy of diagnosis and prediction of technical and economic indicators (TEI) of working drilling equipment in the well (for example, SDM), which helps to reduce the cost of repair, modernization and development of equipment, increase the level of safety and aimed at creating a digital enterprise (holding), which can significantly increase the productivity and competitiveness of enterprises of COG.