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Probabilistic Integration of Geomechanical and Geostatistical Inferences for Mapping Natural Fracture Networks

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Abstract

Geomechanical modeling of the fracturing process accounts for the physical factors that inform the propagation and termination of the fractures. However, the resultant models may not honor the fracture statistics derived from auxiliary sources such as outcrop images. Stochastic algorithms, on the other hand, generate natural fracture maps based purely on statistical inferences from outcrop images excluding the effects of any physical processes guiding the propagation and termination of fractures. This paper, therefore, focuses on presenting a methodology for combining information from geomechanical and stochastic approaches necessary to obtain a fracture modeling approach that is geologically realistic as well as consistent with the geomechanical conditions for fracture propagation. As a prerequisite for this integration approach, a multi-point statistics-based stochastic simulation algorithm is implemented that yields the probability of fracture propagation along various paths. The application and effectiveness of this probability integration paradigm are demonstrated on a synthetic fracture set.

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Acknowledgements

The authors would like to acknowledge the support and funding from the Penn State Initiative for Geostatistics and Geo-Modeling Applications (PSIGGMA) and the member companies.

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Authors and Affiliations

  1. John and Willie Leone Family Department of Energy and Mineral Engineering, Pennsylvania State University, State College, PA, 16801, USA

    Akshat Chandna & Sanjay Srinivasan

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  1. Akshat Chandna
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  2. Sanjay Srinivasan
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Correspondence to Akshat Chandna.

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Appendix A: Integrated Fracture Maps Corresponding to Different Values of \(\tau \)

Appendix A: Integrated Fracture Maps Corresponding to Different Values of \(\tau \)

Fig. 17
figure 17

Fracture set simulated using integration algorithm for \(\tau = 0\)

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Fig. 18
figure 18

Fracture set simulated using integration algorithm for \(\tau = 2.5\)

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Fig. 19
figure 19

Fracture set simulated using integration algorithm for \(\tau = 5\)

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Simulated fracture maps corresponding to the case in which the orientation of initial flaws is sampled from the orientation distribution of conditioning fractures for \(\tau \) values of 0, 2.5, and 5 are shown in Figs. 17a, 18, and  19a. The corresponding histograms of simulated angle classes are plotted against the training angle classes in Figs. 17b, 18 and 19b.

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Chandna, A., Srinivasan, S. Probabilistic Integration of Geomechanical and Geostatistical Inferences for Mapping Natural Fracture Networks. Math Geosci 55, 645–671 (2023). https://doi.org/10.1007/s11004-022-10041-x

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