The most common methods of interpolation are direct, lagrange, newton divided difference, and spline. Each of these techniques has first, second, and third order approximating polynomials that can be used anytime the need for interpolation arises in mathematical analysis. Of all the methods outlined above, the first order approximating polynomials of these interpolation techniques have found great use because of the ease of application. The fact that these polynomials estimate approximate values calls for the need to check the most accurate interpolation method. Accuracy in reservoir modelling and analysis is of great importance to the petroleum industry because business decisions are taken from the outcome of such analysis. Most of these analyses depend on the accuracy of interpolation been employed.
In this paper, some basic PVT parameters were analyzed with both large and few data points. Few data points were used in order to replicate real life scenario since most of the PVT parameters come with few data point after laboratory experiments. For the large data points, all the interpolating techniques irrespective of the order of their approximating polynomials gave a good result but with few data points, different results were obtained. From the results, it was observed that for PVT interpolations, spline third order approximating polynomial performed better than the rest with few data points.
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Chapra, S.C, and Canale, R.P. (2010): Numerical Methods for Engineers 6th Edition, McGraw-Hill Higher Education Inc.
Ertekin, T,Abou-Kassem, J.H, and King, G.R (2001): Basic Applied Reservoir Simulation. Henry Doherty Memorial Fund of AIME, Society of Petroleum Engineers, Richardson, Texas.
Mannon, R.W. (1965): Oil Production Forecasting by Decline Curve Analysis, SPE 1254, Paper presented at 40th Annual Fall Meeting of SPE AIME, held in Denver.
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