A Fuzzy Inference System for Detection of Positive Displacement Motor (PDM) Stalls during Coiled Tubing Operations

Abstract

Positive Displacement Motors (PDM) are extensively used in the oilfield, either in drilling or in coiled tubing (CT) operations. They provide a higher rate of penetration and the possibility of drilling horizontal wells. For coiled tubing operations, PDMs can mill through obstructions and enable shut-in wells to work again. One of the major challenges while using these tools is the motor stalling, which can lead to serious damage to the PDM and lost time events in drilling and workover rigs. These events result in total losses of hundreds of thousands of dollars, and their avoidance mostly depends on trained and fully aware equipment operators. If a PDM starts to stall, the pumping needs to be halted immediately or the tool may fail. This paper describes the use of a Fuzzy Inference System (FIS) to detect the stalling events as they start to happen using the acquisition data from the coiled tubing unit, the output of the FIS could then trigger an alarm for the operator to take the proper action or remotely stop the pump. The FIS was implemented in Python and tested with real CT milling acquisition data. When tested using real data, the system analyzed 68,458 acquisition points and detected 94% of the stalling events across this data during its first seconds, whereas, during the real job, a CT operator could take longer to notice this event and take the proper action, or even take no action. If the FIS was applied on a real coiled tubing acquisition system, it could reduce PDMs over-pressurization due to stalling, leading to an increase on its useful life and decrease on premature failure. As of now there is no similar system in the market or published and this kind of operation is fully performed using human supervision only.