Integrating structural geological data into the inverse modelling framework of iTOUGH2

Abstract

The validity of subsurface flow simulations strongly depends on the accuracy of relevant rock property values and their distribution in space. In realistic simulations, this spatial distribution is based on two geological considerations: (1) the subsurface structural setting, and (2) smaller-scale heterogeneity within a hydrostratigraphic unit. Both aspects are subject to uncertainty, but whereas techniques to address heterogeneity are well established, no general method exists to evaluate the influence of structural uncertainties. We present a method to include structural geological data (e.g. observations of geological contacts and faults) directly into an inversion framework, with the aim of enabling the inversion routine to adapt a full 3-D geological model with a set of geological parameters. In order to achieve this aim, we use a set of Python modules to combine several pre-existing codes into one workflow, to facilitate the consideration of a structural model in the typical model evaluation steps of sensitivity analysis, parameter estimation, and uncertainty propagation analysis. In a synthetic study, we then test the application of these three steps to analyse CO2 injection into an anticlinal structure with the potential of leakage through a fault zone. We consider several parts of the structural setting as uncertain, most importantly the position of the fault zone. We then evaluate (1) how sensitive CO2 arriving in several observation wells would be with respect to the geological parameters, (2) if it would be possible to determine the leak location from observations in shallow wells, and (3) how parametric uncertainty affects the expected CO2 leakage amount. In all these cases, our main focus is to consider the influence of the primary geological data on model outputs. We demonstrate that the integration of structural data into the iTOUGH2 framework enables the inversion routines to adapt the geological model, i.e. to re-generate the entire structural model based on changes in several sensitive geological parameters. Our workflow is a step towards a combined analysis of uncertainties not only in local heterogeneities but in the structural setting as well, for a more complete integration of geological knowledge into conceptual and numerical models.