Multiple‐point‐based geostatistical methods are used to model complex geological structures. However, a training image containing the characteristic patterns of the Earth model has to be provided. If no training image is available, two‐point (i.e., covariance‐based) geostatistical methods are typically applied instead because these methods provide fewer constraints on the Earth model. This study is motivated by the case where 1‐D vertical training images are available through borehole logs, whereas little or no information about horizontal dependencies exists. This problem is solved by developing theory that makes it possible to combine information from multiple‐ and two‐point geostatistics for different directions, leading to a mixed‐point geostatistical model. An example of combining information from the multiple‐point‐based single normal equation simulation algorithm and two‐point‐based sequential indicator simulation algorithm is provided. The mixed‐point geostatistical model is used for conditional sequential simulation based on vertical training images from five borehole logs and a range parameter describing the horizontal dependencies.
Thawing of permafrost due to global warming can have major impacts on hydrogeological processes, climate feedback, arctic ecology, and local environments. To understand these effects and processes, it is crucial to know the distribution of permafrost. In this study we exploit the fact that airborne electromagnetic (AEM) data are sensitive to the distribution of permafrost and demonstrate how the distribution of permafrost in the Yukon Flats, Alaska, is mapped in an efficient (semiautomatic) way, using a combination of supervised and unsupervised (machine) learning algorithms, i.e., Smart Interpretation and K‐means clustering. Clustering is used to sort unfrozen and frozen regions, and Smart Interpretation is used to predict the depth of permafrost based on expert interpretations. This workflow allows, for the first time, a quantitative and objective approach to efficiently map permafrost based on large amounts of AEM data.
When a geologist sets up a geologic model, various types of disparate information may be available, such as exposures, boreholes, and (or) geophysical data. In recent years, the amount of geophysical data available has been increasing, a trend that is only expected to continue. It is nontrivial (and often, in practice, impossible) for the geologist to take all the details of the geophysical data into account when setting up a geologic model. We have developed an approach that allows for the objective quantification of information from geophysical data and borehole observations in a way that is easy to integrate in the geologic modeling process. This will allow the geologist to make a geologic interpretation that is consistent with the geophysical information at hand. We have determined that automated interpretation of geologic layer boundaries using information from boreholes and geophysical data alone can provide a good geologic layer model, even before manual interpretation has begun. The workflow is implemented on a set of boreholes and airborne electromagnetic (AEM) data from Morrill, Nebraska. From the borehole logs, information about the depth to the base of aquifer (BOA) is extracted and used together with the AEM data to map a surface that represents this geologic contact. Finally, a comparison between our automated approach and a previous manual mapping of the BOA in the region validates the quality of the proposed method and suggests that this workflow will allow a much faster and objective geologic modeling process that is consistent with the available data.
Heat storage in the Danish subsurface is gaining increasing interest for optimizing the use of energy resources, but no deep heat storage facilities have yet been established. As an analogue we study the Gassum Formation in the Stenlille structure that is presently used for gas storage. This allows us to discuss geological and technical characteristics of an aquifer relevant for heat storage in Denmark. We develop a 3D model for a high-temperature aquifer thermal energy storage system using analysis of geological core data, sedimentological description, geophysical data including well logs and seismic lines, as well as a finite difference model to calculate the recovery efficiency, heat storage capacity and thermal breakthrough time. Based on geostatistical methods we made three realisations and found similar results for the three cases. In accordance with results from published simplified models we found a high recovery efficiency of 70% after 4 years and 69% after 20 years, a high heat storage capacity of 1.8×1018 J, and a long thermal breakthrough time of 66–77 years. These results reflect the excellent reservoir properties of the Gassum Formation in Stenlille, characterised by a uniformly layered sand/shale sedimentology, a high average porosity of 25% and a high permeability of 1000 to 10 000 mD of sandstone intervals.
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