Progressing cavity pump (PCP) systems drive their name from the unique, positive displacement that evolved from the helical gear pump concept first developed by Rene Moineau in the late of 1920s, they also called screw pump, the PC pump initially were used extensively as fluid transfer pumps in wide range of industrial and manufacturing applications, with some attempt made to use them for the surface transfer of oil fields. The two key features that differentiate PC pump system from other forms of artificial lift are the down hole pc pump and associated surface drive systems also other components such as the production tubing and sucker rod strings, are found in down hole lift system, the design and operational requirement typically differ for PCP applications also many additional equipment component, may be used in conjunction with PCP systems to contend with specific application condition.
In practice, to prevent any production losses and increase the pump lifetime, pumps are maintained periodically. Obviously, excessive manual supervision of the pumps may result in increased labor force demand and increase the spare part costs. However, increased periodic surveillance do not prevent unexpected failures completely. Consequently, developing methods for preventing pump failures and detecting faults ahead of time by utilizing machine learning and artificial intelligence algorithms are becoming essential in the oil and gas industry.
Predictive maintenance aims to transform advanced analytical and process data into valued outcomes. Hence, equipment failure or breakdown can be prevented just before it occurs. Additionally, predictive maintenance may take advantage of ML algorithms to build a systematic approach. Besides, predictive maintenance minimizes the cost of maintenance and improves the equipment lifetime without causing unpredicted production losses. Thus, the process will run as long as possible without interruption.