Model structure and ensemble size: Implications for predictions of groundwater age

Abstract

This paper examines the influence of simplified vertical discretization using 50- to four- layer models and ensemble size on history matching and predictions of groundwater age for a national scale model of New Zealand (approximately 265,000 km2). A reproducible workflow using a combination of opensource tools and custom python scripts is used to generate three models that use the same model domain and underlying data with only the vertical discretization changing between the models. The iterative ensemble smoother approach is used for history matching each model to the same synthetic dataset. The results show that: 1) the ensemble based mean objective function is not a good indicator of model predictive ability, 2) predictive failure from model structural errors in the simplified models are compounded by history matching, especially when small (<100 member) ensembles are used, 3) predictive failure rates increase with iteration, 4) predictive failure rates for the simplified model reach 30–65% using 50-member ensembles, but stabilize at relatively low values (<10%) using the 300 member ensemble, 5) small (50 member) ensembles contribute to predictive failure of 22–30% after six iterations even in structurally “perfect” models, 6) correlation-based localization methods can help reduce prediction failure associated with small ensembles by up to 45%, 7) the deleterious effects of model simplification and ensemble size are problem specific. Systematic investigation of these issues is an important part of the model design, and this investigation process benefits greatly from a scripted, reproducible workflow using flexible, opensource tools.