Geology-driven modeling: A new probabilistic approach for incorporating uncertain geological interpretations in 3D geological modeling

Combining different sources of information about the subsurface is an inherent challenge in process making realistic geological and hydrostratigraphic models. Often available hydrological data from boreholes or outcrops are sparse modeling supplemented spatially with geophysical to obtain a better understanding 3D lithological, structural, relations study area. In traditional modeling, modeler combines all this during consider several factors like e.g., distance neighboring data, consistency between information, uncertainty environment when assigning uncertainties interpretation points. However, assigned subjective can only be communicated qualitatively. The benefit probabilistic model that it enables more quantifiable approach but models usually difficult set up, computationally demanding as well interpret for geologist decision makers. Moreover, there little tradition including knowledge/information directly approaches. following, we utilize interpretations manually interpreted (cognitive) layer input model. A realization created by geostatistical simulation each based on geologist's points corresponding uncertainties. By compiling simulated layers, structural obtained. studying sample such realizations, cognitive derived. We name methodology geology-driven (GDM) rather than directly. tested using sequential Gaussian Denmark. Our results show GDM successfully allows transforming static into full further decisionmakers. proposed updating pre-existing geologically intuitive stochastic framework incorporated current framework.