Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods

Barfod, Adrian A. S.; Møller, Ingelise; Christiansen, Anders Vest; Høyer, Anne‐Sophie; Hoffimann, Júlio; Straubhaar, Julien; Caers, Jef

doi:10.5194/hess-22-3351-2018

Cited by 35 publications

(31 citation statements)

References 67 publications

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“…Generally, the requirements for the geophysical modeling procedure are twofold. Firstly, the categorical TI needs to be populated with resistivity values, e.g., as in Christensen et al (2017) where a Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to create 1-D resistivity models drawn from a posterior probability distribution. This is no straightforward task.…”

Section: Discussionmentioning

confidence: 99%

Contributions to uncertainty related to hydrostratigraphic modeling using multiple-point statistics

Barfod

Vilhelmsen

Jørgensen

et al. 2018

Hydrol. Earth Syst. Sci.

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Abstract. 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.

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Section: Discussionmentioning

confidence: 99%

Contributions to uncertainty related to hydrostratigraphic modeling using multiple-point statistics

Barfod

Vilhelmsen

Jørgensen

et al. 2018

Hydrol. Earth Syst. Sci.

Self Cite

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“…These are common in the stratigraphic progression and typical of shallow marine environments. To describe such variation, we use a 3-D Gaussian process that is truncated (Beucher et al, 1993), thereby generating discrete variables. This truncated Gaussian process has a specific advantage in reproducing simple organizations of ordered lithologies, thus making a useful model in our case.…”

Section: Faciesmentioning

confidence: 99%

Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

2020

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Abstract. Geological uncertainty quantification is critical to subsurface modeling and prediction, such as groundwater, oil or gas, and geothermal resources, and needs to be continuously updated with new data. We provide an automated method for uncertainty quantification and the updating of geological models using borehole data for subsurface developments within a Bayesian framework. Our methodologies are developed with the Bayesian evidential learning protocol for uncertainty quantification. Under such a framework, newly acquired borehole data directly and jointly update geological models (structure, lithology, petrophysics, and fluids), globally and spatially, without time-consuming model rebuilding. To address the above matters, an ensemble of prior geological models is first constructed by Monte Carlo simulation from prior distribution. Once the prior model is tested by means of a falsification process, a sequential direct forecasting is designed to perform the joint uncertainty quantification. The direct forecasting is a statistical learning method that learns from a series of bijective operations to establish “Bayes–linear-Gauss” statistical relationships between model and data variables. Such statistical relationships, once conditioned to actual borehole measurements, allow for fast-computation posterior geological models. The proposed framework is completely automated in an open-source project. We demonstrate its application by applying it to a generic gas reservoir dataset. The posterior results show significant uncertainty reduction in both spatial geological model and gas volume prediction and cannot be falsified by new borehole observations. Furthermore, our automated framework completes the entire uncertainty quantification process efficiently for such large models.

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“…In this section, we check TDS in a three-dimensional cosimulation scenario. This case was investigated in the previous work by Barfod et al (2018). In this work, we focus on a real-world hydrostratigraphic survey from Kasted, Denmark, which covers an area of 45 km 2 .…”

Section: Hydrogeology: Groundwater Management In Denmark 521 Scenamentioning

confidence: 99%

A Tree‐Based Direct Sampling Method for Stochastic Surface and Subsurface Hydrological Modeling

Zuo

Yin

Pan

et al. 2020

Water Resources Research

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Direct sampling (DS) is a versatile multiple‐point statistics method for generating spatial‐temporal geostatistical models. DS is known for being able to address a variety of training images and hence spatiotemporal stochastic modeling problems. One limitation of DS is the central processing unit (CPU) time, mostly attributed to the use of a random search for patterns in the training image. To improve CPU performance, we propose a tree‐based direct sampling (TDS) method. In our method, training patterns are grouped according to their similarities combined with a clustering tree for fast lookup. Rather than patterns, we store locations in our database. During the simulation, TDS applies a tree‐driven search approach. Two objectives, similarity and diversity, are used to rapidly retrieve patterns and prevent trapping into local optima. We also introduce a way to speed up simulation by means of pasting patterns with adaptive size. The performance of our TDS is investigated using a 2‐D benchmark training image. Moreover, we apply the proposed method to two real cases including gap filling the bedrock topography in Antarctica from radar to better understand subglacial hydrology and creating 3‐D groundwater models in the Danish aquifer system. Based on several quantitative evaluations, we find the proposed TDS is comparable to DS in terms of simulation quality, while significantly saves CPU time.

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Hydrostratigraphic modeling using multiple-point statistics and airborne transient electromagnetic methods

Cited by 35 publications

References 67 publications

Contributions to uncertainty related to hydrostratigraphic modeling using multiple-point statistics

Contributions to uncertainty related to hydrostratigraphic modeling using multiple-point statistics

Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)

A Tree‐Based Direct Sampling Method for Stochastic Surface and Subsurface Hydrological Modeling

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