Abstract

As the oil industry begins to emerge from one of the worst downturns in decades, companies need to operate in a market of unstable oil prices that the upstream oil and gas industry continues to face. New innovative, sustainable, and disruptive artificial lift technologies are the cost-effective way seek by the operators to minimize risks, maximize production, and remain profitable on the market.

In wells with sucker rod-lift systems, elastic beam vibrations induced by downhole pump operation are the main source for premature rod string failure and tubing wear. A completely new steady-state vibrations model for rod string assemblies has been implemented to understand and predict the rod/tubing wear damage phenomenon responsible for half of all well failure events in beam pump operation and most expensive routine well servicing cost.

This paper introduces the developed model to enhance prediction of mechanical rod string dynamics during pumping operation, and delivers next generation analytics solution to be utilized for artificial intelligence, industrial internet of things, real-time monitoring, and automation of rod pumping systems.

The model works on a set of forced Duffing-like differential equations with cubic non-linearity and damping, these generated discretizing beam elastic behavior using vibration mode basis. Model simulation runs in the time domain numerically capture relevant axial-flexural dynamic forces undergone by rod string during up and down stroke phases, as well as downstroke rod string bending-buckling tendency and its interaction with tubing internal wall.

Some examples show usefulness of the novel model to predict failures and maximize uptime, reduce costs with predictive analytics, and design the optimal rod lift solution in every stage of well life cycle.

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