Research on appropriate borehole density for establishing reliable geological model based on quantitative uncertainty analysis

Sun, Qing; Shao, Jingli; Wang, Yulong; Ma, Tao

doi:10.1007/s12517-019-4533-7

Cited by 7 publications

(1 citation statement)

References 35 publications

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“…The tsim code and its associated transition probability-based categorical geostatistical methodologies have subsequently been found to be useful to characterization and modeling of a variety of heterogeneous hydrogeological systems (Carle, 2000;Ritzi, 2000;Lu and Zhang, 2002;Bohling and Dubois, 2003;Zhang and Fogg, 2003;Carle et al, 2004;James, 2004;Troldborg, 2004;Weissmann et al, 2004;McDonald et al, 2005;Ye and Khaleel, 2008;Janza, 2009;Sakaki et al, 2009;Alberto, 2010;Engdahl et al, 2010b;Janza, 2009;Doherty and Christensen, 2011;Papapetrou and Theodossiou, 2012;Purkis et al, 2012;Guastaldi et al, 2014;He et al, 2015;Song et al, 2015;Weissmann et al, 2015;Krage et al, 2016;Zhu et al, 2016a;Meirovitz et al, 2017;Muskus and Falta, 2018;Erdal et al, 2019;Sun et al, 2019;Wu et al, 2019;Arshadi et al, 2020). Furthermore, tsim has been applied to the study of a range of problems and processes involving subsurface heterogeneity including groundwater recharge or river flow loss (Tompson et al, 1999;Izbicki, 2002;Fleckenstein et al, 2006;Frei et al, 2009;Engdahl et al, 2010a, Pryshlak et al, 2015Ganot et al, 2018;…”

Section: Introductionmentioning

confidence: 99%

Integration of Soft Data Into Geostatistical Simulation of Categorical Variables

Carle

Fogg

2020

Front. Earth Sci.

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Uncertain or indirect "soft" data, such as geologic interpretation, driller's logs, geophysical logs or imaging, offer potential constraints or "soft conditioning" to stochastic models of discrete categorical subsurface variables in hydrogeology such as hydrofacies. Previous bivariate geostatistical simulation algorithms have not fully addressed the impact of data uncertainty in formulation of the (co) kriging equations and the objective function in simulated annealing (or quenching). This paper introduces the geostatistical simulation code tsim-s, which accounts for categorical data uncertainty through a data "hardness" parameter. In generating geostatistical realizations with tsim-s, the uncertainty inherent to soft conditioning is factored into both 1) the data declustering and spatial correlation functions in cokriging and 2) the acceptance probability for change of category in simulated quenching. The degree or sensitivity to which soft data conditions a realization as a function of hardness can be quantified by mapping category probabilities derived from multiple realizations. In addition to point or borehole data, arrays of data (e.g., as derived from a depth-dependency function, probability map, or "prior realization") can be used as soft conditioning. The tsim-s algorithm provides a theoretically sound and general framework for integrating datasets of variable location, resolution, and uncertainty into geostatistical simulation of categorical variables. A practical example shows how tsim-s is capable of generating a large-scale three-dimensional simulation including curvilinear features.

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