An Evaluation of Assisted History Matching Methodologies for Giant Simulation Models

Abstract

Assisted history matching (AHM) methodologies provide a systematic approach to history match reservoir models accounting for uncertainties. It also provides sensitivity of reservoir response within the uncertainty range of parameters. There are usually large degrees of uncertainties in a simulation model, and as the simulation model becomes very large, both engineering and computational complexities associated with AHM methodologies become massive. The performance an AHM algorithm depends on its ability to provide a solution with an acceptable level of accuracy and uncertainty tolerance and computational efficiency to reach that goal. This study provides performance evaluation guidelines for AHM studies and a cost benefit metrics for feasible history matching studies of giant simulation models. These metrics will take into consideration several criteria, such as the quality of the simulation model, the requirement for compute and storage resources, time to converge to an optimal or acceptable simulation model, user friendliness and ease of integration of the tool in an existing simulation environment. The goal of this evaluation metrics is to assist reservoir engineers to identify the best class of tools and algorithms, which will be appropriate for history matching studies of simulation models. The evaluation matrices were used to evaluate two stochastic tools. One is utilizing genetic/evolutionary algorithms and the other one is using different global statistical algorithms. The study is performed using an oil field in Saudi Arabia. This study identified key strengths and shortcomings of these two classes of algorithms for large scale history matching studies. The paper demonstrates that the current metrics can serve as a suitable screening tool to identify an appropriate methodology to be used in a history matching study.

Conditioning reservoir models on rate data using ensemble smoothers

“…The conditioning on dependent and redundant rate data introduces new potential pitfalls when using ensemble methods compared to previous typical minimization algorithms. Traditionally, we formulated the history-matching problem as the minimization of a cost function that measures the difference between observed and predicted rates computed given a set of uncertain parameters [2,26]. Typically, one would also include a term constraining the deviation from the prior estimate of the parameters.…”