Uncertainty Analysis of SCAL Data

Omidreza Amrollahinasab Mahdiabad, Siroos Azizmohammadi, Pit Arnold, Holger Ott

Research output: Contribution to conferencePosterEducationpeer-review


Standard analytical techniques, which are used to calculate saturation functions from SCAL experiments, are limited to their underlying assumptions. Here we perform uncertainty analysis and inverse modeling, which enables us to have a better interpretation of the physics behind the SCAL experiments. This includes the simultaneous interpretation of capillary pressure (P_C (S_W)) and relative permeability (k_r (S_W)) data, as both saturation functions and experimental data are highly coupled. In this presentation, we show an open-source tool developed based on MATLAB-MRST library for comprehensive interpretation of SCAL data. The tool runs forward simulations using MRST and then matches the simulation predictions and the experimental data by varying the saturation functions using the MATLAB optimization toolbox. Saturation functions are constructed and varied in a point-by-point fashion to overcome the limitations imposed by given parametrization of the models like Corey and LET. Core flooding and centrifuge data are matched simultaneously using a single set of saturation functions and summing up the total error in a single objective function. The uncertainties are then analyzed using the Parallel High-Performance Delayed-Rejection Adaptive Metropolis Markov Chain Monte Carlo method, which provides a sampling tool to explore the response surface, and the uncertainty ranges around the history matching results. The tool utilizes the high computational efficiency provided by MRST and combines it with the parallelization, optimization, and sampling capabilities of the libraries in MATLAB to run the simulations in a fast and efficient way.
Original languageEnglish
Publication statusPublished - 15 Sept 2021

Cite this