Improved Slippage Model for Sucker Rod Pumps Developed in a Pump Test Facility and Verified by Field Measurements

Abstract

Sucker rod pumps provide a cost-efficient way to produce hydrocarbons from low-pressure reservoir formations. Their design is dependent on predictive models used to optimize the system before implementation in the field. The greater the accuracy of these models, the better the performance of the pumping system in the field. The scope of this paper is to present an improved plunger slippage model, developed in connection to the pump test facility (PTF) and validated by field data. This paper provides an analysis of plunger slippage. Existing plunger-slippage models are compared with field data. Based on the result of this comparison, an improved plunger-slippage model is derived based on the Navier-Stokes equation and dimensional analysis. Adjustments are applied to increase the model's validity. The mathematical and laboratory work have shown that a proper fit to reality requires four coefficients that make the equation an empirical one. An extensive laboratory test campaign, using real field equipment, was performed at the PTF at Montanuniversitaet Leoben (MUL). Numerous influencing parameters, such as plunger velocity, clearance magnitude, and fluid viscosity, were studied. Historical plunger-slippage models overestimate the slippage rate, whereas field data showed that newer models underestimate the slippage rate. In general, dynamic models are more accurate than static slippage models. The fit of the four model coefficients, based on laboratory tests, indicate that the chosen strategy of using laboratory tests and allocating the results to field conditions has worked out. The comparison of the results obtained by the presented improved slippage model and the field tests indicate a good match. The presented slippage model predicts the plunger slippage rate precisely and results in greater accuracy. The plunger wear rate approach is presented, which can be used to plan well interventions, decrease intervention costs, and increase the mean time between failures.