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Multiple-Point Geostatistics-Based Three-Dimensional Automatic Geological Modeling and Uncertainty Analysis for Borehole Data

Abstract

The three-dimensional characterization of geological structures is important for determining the distribution of subsurface mineral resources. However, geological structures and geological phenomena have great stochasticity and uncertainty at the microscopic level. Traditional multiple-point geostatistics use mostly three-dimensional conceptual models or two-dimensional sections as training images, while simulations using direct low-dimensional borehole data are lacking. In this paper, we propose a new multiple-point geostatistical method to automatically reconstruct three-dimensional geological models directly from borehole data, which can significantly reduce the complexity of intermediate manual operations. First, the geological structure characteristics in the borehole data are extracted, and then an initial prior model is constructed based on geological constraints. Next, for the non-stationary problem, the mobile local scan approach is proposed to make the simulation nodes scan in a certain range of scaled area to simultaneously achieve the zonal simulation effect and eliminate the discontinuity problem between zonal boundaries. Based on this solution, the whole modeling workflow is designed. Finally, the algorithm is validated using actual plains area geological survey data, compared to other modeling methods, and evaluated for model uncertainty. The results show that the proposed 3D geological modeling method can effectively expose the stratigraphic structural morphology, stratigraphic attributes and overburden relationships. It will provide decision support for resource exploration and reduce exploration costs.

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Acknowledgments

This research was financially supported by the National Natural Science Foundation of China (Nos. 42172327 and 41671404), the Fundamental Research Funds for the Central Universities (N2201022), and China Geological Survey Projects (DD20190416). We are very grateful to the editor and two anonymous reviewers for their insightful comments and suggestions, which led to the improvement of the manuscript.

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Correspondence to Jiateng Guo.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Guo, J., Wang, Z., Li, C. et al. Multiple-Point Geostatistics-Based Three-Dimensional Automatic Geological Modeling and Uncertainty Analysis for Borehole Data. Nat Resour Res (2022). https://doi.org/10.1007/s11053-022-10071-6

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  • DOI: https://doi.org/10.1007/s11053-022-10071-6

Keywords

  • 3D geological modeling
  • Multiple-point geostatistics
  • Borehole data
  • Uncertainty analysis
  • Resource reconnaissance