Interpolation Method for Modeling Petrophysical Properties based on a Truncated Gaussian Kernel

Authors

DOI:

https://doi.org/10.5281/zenodo.18149001

Keywords:

Truncated Gaussian Kernel, interpolation, geostatistics, petrophysical modeling

Abstract

This paper presents the methodological development, formulation, and validation of a new deterministic interpolator: the Truncated Gaussian Kernel (TGK), which is based on the principles of Smoothed Particle Hydrodynamics (SPH). It is a mesh-free method designed to handle the dispersed and heterogeneous data common in hydrocarbon exploration and production. The proposed methodology includes a robust process for optimizing its hyper parameters by minimizing the Mean Squared Error (MSE). The performance of the KGT is rigorously validated through statistical tests and comparison with Universal Kriging in a study area. The results demonstrate a significant improvement in predictive accuracy and a high capacity for generalization in blind tests, reducing over fitting and thus establishing the KGT as an accurate and robust alternative for modeling petrophysical properties.

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Author Biographies

  • Efraín Alberto Daubront Yumar, Corporación Venezolana del Petróleo, Caracas, Venezuela

    Corporación Venezolana del Petróleo, Caracas, Venezuela

  • Carlos Márquez, Universidad Venezolana de los Hidrocarburos (UVH), Caracas, Venezuela

    Universidad Venezolana de los Hidrocarburos (UVH), Caracas, Venezuela

References

Amicarelli, A., & Sigalotti, L. D. G. (2025). Dispersion analysis of SPH for parabolic equations: High-order kernels against tensile instability. Journal of Computational Physics, 510, 113075. https://doi.org/10.1016/j.jcp.2024.113075

Bui, H. H., Fukagawa, R., & Sako, K. (2007). SPH-based numerical simulation of soil-water interaction. International Journal for Numerical and Analytical Methods in Geomechanics, 31(8), 1045-1066.

Duvenaud, D., Lloyd, J. R., Grosse, R., Tenenbaum, J. B., & Ghahramani, Z. (2013). Gaussian Process Kernels for Pattern Discovery and Extrapolation. Advances in Neural Information Processing Systems, 26.

Evensen, G. (2003). The Ensemble Kalman Filter: theoretical formulation and practical implementation. Ocean Dynamics, 53(4), 343-367.

Gingold, R. A., & Monaghan, J. J. (1977). Smoothed particle hydrodynamics: theory and application to non-spherical stars. Monthly Notices of the Royal Astronomical Society, 181, 375-389.

Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.

Görtler, J., Pösl, C., & Weiskopf, D. (2019). Gaussian kernels and other locally supported models. En Kernel-Based Approximation Methods Using MATLAB (pp. 55-74). World Scientific Publishing.

Haldorsen, H. H., & Damsleth, E. (1990). Stochastic modeling. Journal of Petroleum Technology, 42(4), 404-412.

Isaaks, E. H., & Srivastava, R. M. (1989). An Introduction to Applied Geostatistics. Oxford University Press.

Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th ed.). Pearson Prentice Hall.

Journel, A. G., & Huijbregts, C. J. (1978). Mining Geostatistics. Academic Press.

Li, Y., & Wu, H. (2018). Gaussian Process Kernels for Support Vector Regression in Wind Energy Prediction. Journal of Renewable and Sustainable Energy, 10(4), 043301. https://doi.org/10.1063/1.5035118

Lucy, L. B. (1977). A numerical approach to the testing of the fission hypothesis. The Astronomical Journal, 82, 1013-1024.

Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246-1266.

Pérez, R., García, L., & Rodríguez, F. (2019). Estudio de transformadas multi-atributo para la predicción de propiedades petrofísicas a partir de datos sísmicos en un yacimiento de la Cuenca del Golfo de México. Revista de Ingeniería Petrolera, 59(2), 45-58.

Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. The MIT Press.

Xu, X., & Haq, B. U. (2022). Seismic facies analysis: Past, present and future. Earth-Science Reviews, 224, 103876. https://doi.org/10.1016/j.earscirev.2021.103876

Published

2026-01-05

How to Cite

Interpolation Method for Modeling Petrophysical Properties based on a Truncated Gaussian Kernel. (2026). PetroRenova Indexed, 2(1), 51-71. https://doi.org/10.5281/zenodo.18149001