Troels Norvin Vilhelmsen

Joint inversion of aquifer test, MRS, and TEM data

This paper presents two methods for joint inversion of aquifer test data, magnetic resonance sounding (MRS) data, and transient electromagnetic data acquired from a multilayer hydrogeological system. The link between the MRS model and the groundwater model is created by tying hydraulic conductivities (k) derived from MRS parameters to those of the groundwater model. Method 1 applies k estimated from MRS directly in the groundwater model, during the inversion. Method 2 on the other hand uses the petrophysical relation as a regularization constraint that only enforces k estimated for the groundwater model to be equal to MRS derived k to the extent that data can be fitted. Both methodologies can jointly calibrate parameters pertaining to the individual models as well as a parameter pertaining to the petrophysical relation. This allows the petrophysical relation to adapt to the local conditions during the inversion. The methods are tested using a synthetic data set as well as a field data set. In combination, the two case studies show that the joint methods can constrain the inversion to achieve estimates of k, decay times, and water contents for a leaky confined aquifer system. We show that the geophysical data can assist in determining otherwise insensitive k, and vice versa. Based on our experiments and results, we mainly advocate the future application of method 2 since this seems to produce the most reliable results, has a faster inversion runtime, and is applicable also for linking k of 3-D groundwater flow models to multiple MRS soundings.

Climate change impacts on groundwater hydrology – where are the main uncertainties and can they be reduced?

ABSTRACT This paper assesses how various sources of uncertainty propagate through the uncertainty cascade from emission scenarios through climate models and hydrological models to impacts, with a particular focus on groundwater aspects from a number of coordinated studies in Denmark. Our results are similar to those from surface water studies showing that climate model uncertainty dominates the results for projections of climate change impacts on streamflow and groundwater heads. However, we found uncertainties related to geological conceptualization and hydrological model discretization to be dominant for projections of well field capture zones, while the climate model uncertainty here is of minor importance. How to reduce the uncertainties on climate change impact projections related to groundwater is discussed, with an emphasis on the potential for reducing climate model biases through the use of fully coupled climate–hydrology models. Editor D. Koutsoyiannis; Associate editor not assigned

Contributions to uncertainty related to hydrostratigraphic modeling using Multiple-Point Statistics

Forecasting the flow of groundwater requires a hydrostratigraphic model, which describes the architecture of the subsurface. State-of-the-art multiple-point statistical (MPS) tools are readily available for creating models depicting subsurface geology. We present a study of the impact of key parameters related to stochastic MPS simulation of a real-world hydrogeophysical dataset from Kasted, Denmark, using the snesim algorithm. The goal is to study how changes to the underlying datasets propagate into the hydrostratigraphic realizations when using MPS for stochastic modeling. This study focuses on the sensitivity of the MPS realizations to the geophysical soft data, borehole lithology logs, and the training image (TI). The modeling approach used in this paper utilizes a cognitive geological model as a TI to simulate ensemble hydrostratigraphic models. The target model contains three overall hydrostratigraphic categories, and the MPS realizations are compared visually as well as quantitatively using mathematical measures of similarity. The quantitative similarity analysis is carried out exhaustively, and realizations are compared with each other as well as with the cognitive geological model. The results underline the importance of geophysical data for constraining MPS simulations. Relying only on borehole data and the conceptual geology, or TI, results in a significant increase in realization uncertainty. The airborne transient electromagnetic SkyTEM data used in this study cover a large portion of the Kasted model area and are essential to the hydrostratigraphic architecture. On the other hand, the borehole lithology logs are sparser, and 410 boreholes were present in this study. The borehole lithology logs infer local changes in the immediate vicinity of the boreholes, thus, in areas with a high degree of geological heterogeneity, boreholes only provide limited large-scale structural information. Lithological information is, however, important for the interpretation of the geophysical responses. The importance of the TI was also studied. An example was presented where an alternative geological model from a neighboring area was used to simulate hydrostratigraphic models. It was shown that as long as the geological settings are similar in nature, the realizations, although different, still reflect the hydrostratigraphic architecture. If a TI containing a biased geological conceptualization is used, the resulting realizations will resemble the TI and contain less structure in particular areas, where the soft data show almost even probability to two or all three of the hydrostratigraphic units.

Extending Data Worth Analyses to Select Multiple Observations Targeting Multiple Forecasts

Hydrological models are often set up to provide specific forecasts of interest. Owing to the inherent uncertainty in data used to derive model structure and used to constrain parameter variations, the model forecasts will be uncertain. Additional data collection is often performed to minimize this forecast uncertainty. Given our common financial restrictions, it is critical that we identify data with maximal information content with respect to forecast of interest. In practice, this often devolves to qualitative decisions based on expert opinion. However, there is no assurance that this will lead to optimal design, especially for complex hydrogeological problems. Specifically, these complexities include considerations of multiple forecasts, shared information among potential observations, information content of existing data, and the assumptions and simplifications underlying model construction. In the present study, we extend previous data worth analyses to include: simultaneous selection of multiple new measurements and consideration of multiple forecasts of interest. We show how the suggested approach can be used to optimize data collection. This can be used in a manner that suggests specific measurement sets or that produces probability maps indicating areas likely to be informative for specific forecasts. Moreover, we provide examples documenting that sequential measurement election approaches often lead to suboptimal designs and that estimates of data covariance should be included when selecting future measurement sets.

Successful Sampling Strategy Advances Laboratory Studies of NMR Logging in Unconsolidated Aquifers

The nuclear magnetic resonance (NMR) technique has become popular in groundwater studies because it responds directly to the presence and mobility of water in a porous medium. There is a need to conduct laboratory experiments to aid in the development of NMR-hydraulic conductivity models, as is typically done in the petroleum industry. However, the challenge has been obtaining high-quality laboratory samples from unconsolidated aquifers. At a study site in Denmark, we employed sonic drilling, which minimizes the disturbance of the surrounding material, and extracted twelve 7.6-cm diameter samples for laboratory measurements. We present a detailed comparison of the acquired laboratory- and logging-NMR data. The agreement observed between the laboratory and logging data suggests that the methodologies proposed in this study provide good conditions for studying NMR measurements of unconsolidated near-surface aquifers. Finally, we show how laboratory sample size and condition impact the NMR measurements.

Addressing Current Challenges on Groundwater Model Structure through Effective Use of Geophysical Data

We wish to present a method for effective generation of structural models for groundwater flow simulations. The methodology is presented for two cases. A regional scale test, where geophysical data and borehole data is used for generating the regional scale hydrostratigraphy, and a local detailed case, where the same methodology is used to address the question of structural uncertainty.