Low oil prices at the stock market and high water cuts of the produced fluid, force the oil companies to continuously optimize their facilities to meet the actual requirements regarding efficiency. One step in the chain of oil production from the reservoir to the customer is the optimization of artificial lift systems in mature oil fields. Nowadays sucker rod pumping systems are still by far the most frequently used artificial lift systems. These pumps represent an efficient and simple way to increase the oil production. On the one hand from the technical point of view, this system is easily adjustable to changing operating conditions. On the other hand, its economic footprint is, compared to other artificial lift methods, relatively small. Due to the huge number of installed units, an energy efficient and failure resistant design is essential. A deep understanding of the actual process during pumping is significant for continuously optimizing the pumping system. This paper presents the evaluation of volumetric efficiency tests, performed at the Pump Testing Facility (PTF) at the Montanuniversität Leoben, under various operating conditions and the investigating of the related physical effects. Slippage, one of the most influencing factors of volumetric efficiency, is highly dependent on the differential pressure generated by the pump plunger, but as well on the strokes per minute. The results of various attempts that were performed to get a detailed understanding of the internal losses (slippage volume) during the pumping operation are shown. Two different pump types, namely the standard sucker rod pump (SRP) and the sucker rod anti-buckling system pump (SRABS) are tested with different fluid types. Both pumps were operated with various speeds and under different pressure conditions. The test results are compared with existing slippage models. The comparison indicates that most of the existing slippage models underpredict the slippage rate by a significant fraction. Therefore, it is necessary to do additional and more detailed tests to be able to define a more precise model to predict slippage losses during pumping operations.
Summary 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.
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