Main motivation for this work is the need for performance evaluation of swelling (and inert) elastomer seals used in petroleum applications. Closed-form (analytical) solutions are derived for sealing pressure distribution along the elastomer seal as a function of material properties of the elastomer, seal geometry and dimensions, seal compression, and differential fluid pressure acting on the seal ends. Seal performance is also modeled and simulated numerically. Good agreement between analytical and numerical results gives confidence that the analytical solution can be used for reliable prediction of sealing behavior of the elastomer. Detailed investigation is then carried out to find out the effect of variation in seal design parameters on seal performance. For both analytical and numerical models, properties of the seal material at various stages of swelling are needed. Therefore, a series of experiments were also designed and conducted to study the effect of swelling on mechanical properties (E, G, K, and ν) of the sealing material.

One major finding is that sealing pressure distribution along the seal is not constant but varies nonlinearly depending on seal parameters and loading conditions, with maximum sealing pressure occurring at the center of the seal length. Longer seals are not necessarily better; after a certain seal length, sealing pressure reaches a steady value for a given set of field conditions. As expected, higher seal compression gives higher sealing pressure. Seal compression can be increased either by tubular expansion or by selecting an elastomer that swells more, or a combination of the two.

Experimental evaluation of swelling-elastomer seal performance can be very costly, and is not even possible in many cases. Numerical simulations, if validated, can be more convenient, but computational effort and cost can be high as simulations have to be run for each set of conditions. Analytical approach presented here not only gives an elegant closed-form solution, but can give reasonably accurate and much faster prediction of elastomer performance under various actual oil and gas field conditions.

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