Assessment of Uncertainty Assessment Methods (2001)

Assessment of Uncertainty Assessment Methods Ning Liu; Ning Liu University of Tulsa Search for other works by this author on: This Site Google Scholar S. Betancourt; S. Betancourt University of Tulsa Search for other works by this author on: This Site Google Scholar D.S. Oliver D.S. Oliver University of Tulsa Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, September 2001. Paper Number: SPE-71624-MS https://doi.org/10.2118/71624-MS Published: September 30 2001 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Liu, Ning, Betancourt, S., and D.S. Oliver. "Assessment of Uncertainty Assessment Methods." Paper presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, September 2001. doi: https://doi.org/10.2118/71624-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search AbstractUncertainty in future reservoir performance is usually evaluated from the simulated performance of a small number of reservoir models. Unfortunately, most of the methods for generating reservoir models conditional to production data are known to create a distribution of realizations that is only approximately correct. The adequacy of the approximations is unknown, although several previous investigations of the approximate algorithms have suggested that the distributions of realizations could be badly misleading. In this paper, we evaluate the ability of the various sampling methods to correctly assess the uncertainty in reservoir predictions by comparing the distribution of realizations with a standard distribution from a Markov chain Monte Carlo method.This study compares the ensemble of realizations from five sampling algorithms for a synthetic, one-dimensional, single-phase flow problem, in order to establish the best algorithm under controlled conditions. The small test problem was chosen in order that a large enough number of realizations could be generated from each method to ensure the statistical validity of the comparisons. Five thousand realizations were generated from each of the approximate sampling algorithms. The approximate sampling methods evaluated were linearization about the maximum a posteriori model (the square-root of the covariance matrix method), randomized maximum likelihood, and two two pilot point methods with six and nine pilot points locations. Realizations were also generated by a Markov chain Monte Carlo method with local perturbations and an attempt was made to generate realizations from a rejection sampling algorithm. The distributions of realizations from the approximate methods were compared to the distributions from the exact methods. While the approximate sampling methods performed relatively well for evaluating uncertainty in average reservoir porosity and effective steady-state permeability, most failed to adequately assess uncertainty in some other function of the reservoir model such as the distribution of extreme permeability values or the data mismatch. In general, the method of randomized maximum likelihood performed better than other approximate methods.IntroductionThe only practical methods for quantifying uncertainty in reservoir performance require the generation of multiple random reservoir models conditional to available data. By simulating the future production from each realization, an empirical distribution of production characteristics is obtained. The validity of this method for quantifying uncertainty depends strongly on the quality of the distribution of reservoir models generated. Methods for sampling from the a posteriori probability density function (pdf) of reservoir flow models conditioned to production data have been widely reported in the literature. Rigorous methods of sampling from the a posteriori distribution for reservoir properties have been applied by Oliver et al.1; Bonet-Cunha et al.2 and Omre et al.3 Most other attempts to quantify uncertainty in reservoir performance are based on approximate sampling algorithms. The purpose of this study is to evaluate the distribution of samples from several of these approximate methods. The same assumptions and models were used for all methods in this study, as differences in the model assumptions have made it difficult to draw quantitative conclusions on the reliability of the methods in previous studies4;5;6. Keywords: upstream oil & gas, sequence, mismatch, pilot point method, history matching, permeability, algorithm, uncertainty assessment method spe 71624, covariance, reservoir simulation Subjects: Reservoir Characterization, Reservoir Simulation, Formation Evaluation & Management, Geologic modeling, Evaluation of uncertainties, History matching This content is only available via PDF. 2001. Society of Petroleum Engineers You can access this article if you purchase or spend a download.