Three-Dimensional Non-Multi-Gaussian Simulation by Including Multiple Types of Information at Non-Colocated Locations

<div> <p><span>The incentive of this presentation is the age-old quest of stochastic hydrogeology: Are we able to better match observed long-tailed breakthrough curves by an improved description of the spatial dependence of saturated hydraulic conductivity (<em>K</em>)?</span></p> </div><div> <p><span> </span></p> </div><div> <p><span>This contribution considers two innovations: We include more information than usual by incorporating multiple types of observations at non-collocated locations (<em>data fusion</em>), and we extract more information than usual from the available measurements by analysing statistical properties that go further than typical second-order moments-based analyses (<em>non-Gaussian geostatistics</em>).</span></p> </div><div> <p><span> </span></p> </div><div> <p><span>The evaluation of these innovations in geostatistical simulation methodologies of spatially distributed fields of <em>K</em> is performed against real-world tracer-tests that were performed at the site of the <em>K</em> measurements. The hypothesis is that fields that contain the most information match the observed solute spreading best.</span></p> </div><div> <p><span> </span></p> </div><div> <p><span>The spatially distributed <em>K</em>- fields were geostatistically simulated using the multi-objective phase annealing (<em>PA</em>) method. To accelerate the asymmetry updating during the PA iterations, a Fourier transform based algorithm is integrated into the three-dimensional PA method. Multiple types of objective functions are included to match the value and/or the order of observations as well as the degree of the “non-Gausianness” (asymmetry). Additionally, “censored measurements” (e.g., high-K measurements above the sensitivity of the device that measures <em>K</em>) are considered.</span></p> </div><div> <p><span> </span></p> </div><div> <p><span>The MAcroDispersion Experiment (MADE) site is considered the holy grail of stochastic hydrogeology as among the well instrumented sites in the world, the variance of the hydraulic conductivity measurements at the MADE site is fairly large and detailed observations of solute spreading are available. In addition to the classic <em>K</em>-measurements obtained via 2611 flowmeter measurements, recently a large set of 31123 <em>K</em>‑measurements obtained via direct push injection logging (DPIL), are available, although not at the same locations where the flowmeter measurements were taken.</span></p> </div><div> <p><span> </span></p> </div><div> <p><span>The influence of including different types of information on the simulated spatially-distributed fields of <em>K</em> are evaluated by analyzing the ensemble spatial moments and the dispersivity of numerical conservative solute tracer tests performed using particle tracking. The improved dependence structure of <em>K</em> with all of the above knowledge contains more information than fields simulated by traditional geostatistical algorithms and expected as a more realistic realization of <em>K</em> at the MADE site and at many other sites where such data-fusion approaches are necessary.</span></p> </div>

In Honour of Distinguished Scientist and Seminal Hydrogeologist Professor Ghislain de Marsily

<p>Professor Ghislain de Marsily is an internationally renowned scientist famed for his contributions to groundwater hydrology and water management. He is a pioneer in the development of stochastic hydrogeology. This presentation will outline and explore the significant contributions made by de Marsily to hydrogeology as a whole and to stochastic hydrogeology in particular. It will examine the effect of his work on defining the discipline of hydrogeology as we know it today, and will go on to show the significant impact his students and colleagues continue to have, inspired by his passion, ideas and enthusiasm for a more sustainable, equitable future for all.</p>

Stochastic hydrogeology's biggest hurdles analyzed and its big blind spot

This paper considers questions related to the adoption of stochastic methods in hydrogeology. It looks at factors affecting the adoption of stochastic methods including environmental regulations, financial incentives, higher education, and the collective feedback loop involving these factors. We begin by evaluating two previous paper series appearing in the stochastic hydrogeology literature, one in 2004 and one in 2016, and identifying the current thinking on the topic, including the perceived data needs of stochastic methods, the attitude in regulations and the court system regarding stochastic methods, education of the workforce, and the availability of software tools needed for implementing stochastic methods in practice. Comparing the state of adoption in hydrogeology to petroleum reservoir engineering allowed us to identify quantitative metrics on which to base our analysis. For impediments to the adoption of stochastic hydrology, we identified external factors as well as self-inflicted wounds. What emerges is a picture much broader than current views. Financial incentives and regulations play a major role in stalling adoption. Stochastic hydrology's blind spot is in confusing between uncertainty with risk and ignoring uncertainty. We show that stochastic hydrogeology comfortably focused on risk while ignoring uncertainty, to its own detriment and to the detriment of its potential clients. The imbalance between the treatment on risk on one hand and uncertainty on the other is shown to be common to multiple disciplines in hydrology that interface with risk and uncertainty.

Stochastic Hydrogeology's Biggest Hurdles Analyzed and Its Big Blind Spot

This paper considers questions related to the adoption of stochastic methods in hydrogeology. It looks at factors affecting the adoption of stochastic methods including environmental regulations, financial incentives, higher education, and the collective feedback loop involving these factors. We begin by evaluating two previous paper series appearing in the stochastic hydrogeology literature, one in 2004 and one in 2016, and identifying the current thinking on the topic, including the perceived data needs of stochastic methods, the attitude in regulations and the court system regarding stochastic methods, education of the workforce and the availability of software tools needed for implementing stochastic methods in practice. Comparing the state of adoption in hydrogeology to petroleum reservoir engineering allowed us to identify quantitative metrics on which to base our analysis. For impediments to the adoption of stochastic hydrology, we identified external factors as well as self-inflicted wounds. What emerges is a picture much broader than current views. Financial incentives and regulations play a major role in stalling adoption. Stochastic Hydrology's blind spot is in confusing between risk and uncertainty and ignoring uncertainty. We show that stochastic hydrogeology comfortably focused on risk while ignoring uncertainty, to its own detriment and to the detriment of its potential clients. The imbalance between the treatment on risk on one hand and uncertainty on the other is shown to be common to multiple disciplines in hydrology that interface with risk and uncertainty.

The effect of training image and secondary data integration with multiple-point geostatistics in groundwater modelling

Multiple-point geostatistical simulation (MPS) has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from a training image (TI). However, its application in three-dimensional (3-D) simulations has been constrained by the difficulty of constructing a 3-D TI. The object-based unconditional simulation program TiGenerator may be a useful tool in this regard; yet the applicability of such parametric training images has not been documented in detail. Another issue in MPS is the integration of multiple geophysical data. The proper way to retrieve and incorporate information from high-resolution geophysical data is still under discussion. In this study, MPS simulation was applied to different scenarios regarding the TI and soft conditioning. By comparing their output from simulations of groundwater flow and probabilistic capture zone, TI from both sources (directly converted from high-resolution geophysical data and generated by TiGenerator) yields comparable results, even for the probabilistic capture zones, which are highly sensitive to the geological architecture. This study also suggests that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data (SkyTEM), but over-conditioning has to be avoided.

The effect of training image and secondary data integration with multiple-point geostatistics in groundwater modeling

Multiple-point geostatistic simulation (MPS) has recently become popular in stochastic hydrogeology, primarily because of its capability to derive multivariate distributions from the training image (TI). However, its application in three dimensional simulations has been constrained by the difficulty of constructing 3-D TI. The object-based TiGenerator may be a useful tool in this regard; yet the sensitivity of model predictions to the training image has not been documented. Another issue in MPS is the integration of multiple geophysical data. The best way to retrieve and incorporate information from high resolution geophysical data is still under discussion. This work shows that TI from TiGenerator delivers acceptable results when used for groundwater modeling, although the TI directly converted from high resolution geophysical data leads to better simulation. The model results also indicate that soft conditioning in MPS is a convenient and efficient way of integrating secondary data such as 3-D airborne electromagnetic data, but over conditioning has to be avoided.