Abstract
The inversion of laboratory centrifuge data to obtain capillary pressure functions in petroleum science leads to a Volterra integral equation of the first kind with a right-hand side defined by a set of discrete data. The problem is ill-posed in the sense of Hadamard [4]. The discrete data lead to a discretized equation of the form
, where b→ represents the observation vector, A is an ill-conditioned matrix derived from the forward problem, c→ is the coefficients in a representation of the inverse capillary function, i.e., parameters to be determined, and ∈→ is the error vector associated with b→. If ∈→∼N(0,σ2), and satisfies the Gauss-Markov (G-M) conditions, then an estimate, c→ λ, of c→ is BLUE [9]. In the presence of outliers, the G-M conditions and/or the normality assumption can be violated.
In this paper we parameterize the capillary pressure function using B-splines and address the issue of ill-posedness by reformulating the problem as a constrained optimization task involving the determination of the spline coefficients. By the nature of the experimental procedure, we expect the G-M conditions to be satisfied. A systematic method of outlier elimination and a choice of knots is employed to ensure satisfaction of the normality assumption and thereby derive capillary pressure curves to a high degree of accuracy. A robust method for estimating the solution curve, which accommodates both outliers and influential points, namely the L 1-norm solution, is also presented. The method is demonstrated on synthetic data.
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References
Bentsen, R. G. and Anli, J., Using parameter sstimation technique to convert centrifuge data into a capillary pressure curve, SPEJ, Feb. 1977, 57–71.
Blom, G., Statistical Estimates and Transformed Beta Variates, New York Wiley, 1958.
Gustafson, S.-Å, Regularizing a Volterra integral equation problem by means of convex programming, Working Paper Nr. 138, 1991.
Hadamard, J., Lectures on the Cauchy Problem in Linear Partial Differential Equations, Yale University Press, 1923.
Hassler, G. L. and Brunner, E., Measurements of sapillary pressures in small core samples, Trans. AIME 160 (1945), 114–121.
Madansky, A., Prescription for Working Statisticians, Springer-Verlag, 1988.
Schumaker, L. L., Spline Functions: Basic Theory, J. Wiley & Sons, New York City, 1981.
Shapiro, S. S. and Wilk, M. B., An analysis of variance test for normality (complete samples), Biometrika 52 (1965), 519–611.
Srivasta, S. and Sen, A., Regression Analysis-Theory, Methods, and Application, Springer-Verlag, 1990.
Subbey, S. and Nordtvedt, J.-E., Estimating sapillary pressure functions from centrifuge data, submitted to AIChe.
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© 2000 Springer-Verlag Berlin Heidelberg
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Subbey, S., Nordtvedt, JE. (2000). Estimation in the Presence of Outliers: The Capillary Pressure Case. In: Chen, Z., Ewing, R.E., Shi, ZC. (eds) Numerical Treatment of Multiphase Flows in Porous Media. Lecture Notes in Physics, vol 552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45467-5_25
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DOI: https://doi.org/10.1007/3-540-45467-5_25
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