Improving Forecast Uncertainty Quantification by Incorporating Production History and Using a Modified Ranking Method of Geostatistical Realizations

Abstract

The majority of the geostatistical realizations ranking methods disregard the production history in selection of realizations, due to its requirement of high simulation run time. They also ignore to consider the degree of linear relationship between the “ranks based on the ranking measure” and “ranks based on the performance parameter” in choosing the employed ranking measure. To address these concerns, we propose an uncertainty quantification workflow, which includes two sequential stages of history matching and realization selection. In the first stage, production data are incorporated in the uncertainty quantification procedure through a history matching process. A fast simulator is employed to find the realizations with consistent flow behavior with the production history data in shorter time, compared to a comprehensive simulator. The selected realizations are the input of the second stage of the workflow, which can be any type of the realization selection method. In this study, we used the most convenient realization selection method, i.e., ranking of the realizations. To select the most efficient ranking measure, we investigated the degree of the linear correlation between the ranks based on the several ranking measures and the performance parameter. In addition, due to the shortcomings of the traditional ranking methods in uncertainty quantiles identification, a modified ranking method is introduced. This modification increases the certainty in the probability of the selected realizations. The obtained results on 3D close-to-real synthetic reservoir models revealed the capability of the modified ranking method in more accurate quantification of the uncertainty in reservoir performance prediction.

A novel methodology for fast reservoir simulation of single-phase gas reservoirs using machine learning

“…In this study, a fast reservoir simulator was used for the history matching process to obtain insights with consistent flow behavior using production history data in a shorter time. The study involved many complex mathematical steps that required the employment of substantial computational resources, and a significant reduction in computing time is not observed, ( Monfaredi et al, 2020 ).…”