A multi-resolution workflow to generate high-resolution models constrained to dynamic data

Abstract

Distance-based stochastic techniques have recently emerged in the context of ensemble modeling, in particular for history matching, model selection and uncertainty quantification. Starting with an initial ensemble of realizations, a distance between any two models is defined. This distance is defined such that the objective of the study is incorporated into the geological modeling process, thereby potentially enhancing the efficacy of the overall workflow. If the intent is to create new models that are constrained to dynamic data (history matching), the calculation of the distance requires flow simulation for each model in the initial ensemble. This can be very time consuming, especially for high-resolution models. In this paper, we present a multi-resolution framework for ensemble modeling. A distance-based procedure is employed, with emphasis on the rapid construction of multiple models that have improved dynamic data conditioning. Our intent is to construct new high-resolution models constrained to dynamic data, while performing most of the flow simulations only on upscaled models. An error modeling procedure is introduced into the distance calculations to account for potential errors in the upscaling. Based on a few fine-scale flow simulations, the upscaling error is estimated for each model using a clustering technique. We demonstrate the efficiency of the method on two examples, one where the upscaling error is small, and another where the upscaling error is significant. Results show that the error modeling procedure can accurately capture the error in upscaling, and can thus reproduce the fine-scale flow behavior from coarse-scale simulations with sufficient accuracy (in terms of uncertainty predictions). As a consequence, an ensemble of high-resolution models, which are constrained to dynamic data, can be obtained, but with a minimum of flow simulations at the fine scale.

Oil production uncertainty assessment by predicting reservoir production curves and confidence intervals from arbitrary proxy responses

“…For this reason, combining proxy evaluation on all models and accurate flow simulation on a few models seems to provide a good compromise to obtain representative uncertainty assessment while harnessing computational costs (Doherty and Christensen, 2011;Effendiev et al, 2009;Ginsgourger et al, 2013;Josset and Lunati, 2013;Josset et al, 2015;Maschio and Schiozer, 2014;Scheidt and Caers, 2009;Scheidt et al, 2011). Yet, the choice of a particular proxy can be consequential in practical case studies, and it is difficult to assess which proxy provides the best compromise between the performance and the quality of uncertainty assessment, which may vary from one case study to another.…”

Flow-based dissimilarity measures for reservoir models: a spatial-temporal tensor approach

“…In [30] and in [31], the total oil production and water rates are used as dissimilarity measures to assess the dynamical responses of different reservoir realizations. Although these measures are less coarse than the NPV, oil and water production rates (combined with pressure measurements) only provide local information of the flow behavior around the wellbore, which cannot be extrapolated to characterize the flow performance of spatial locations far from the production sites.…”

Cokriging for multivariate Hilbert space valued random fields: application to multi-fidelity computer code emulation

“…There are many applications and expansions of this concept up to date. Some of the most notable ones for computer models that produce scalar outputs [Scheidt et al, 2011, Ginsbourger et al, 2013, and for computer models that produce functional outputs [Josset et al, 2015, Trehan et al, 2017, Pagani et al, 2017].…”