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Abstract

Summary

Fracturing horizontal wells is an important technology that can make production from tight and shale formations economical. The fractured tight and shale formations are recognized by complex fracture networks around the primary hydraulic fractures. Microseismic mapping is a technique which can shed light on the activities happen around the main fractures which can direct us towards the extent of the fracture half-length and the secondary fracture networks in the stimulated reservoir volume (SRV). However, microseismic mapping does not necessarily indicate if the observed events can be directly related to the increased conductivities around the wellbore. There is rather a large uncertainty about the interpretation of the extent of effective (reopened) fracture network which can have a large impact on the performance of the flow simulations.

In this paper, a quantitative workflow is attempted to model the discrete fracture networks using multiple-point geostatistical algorithms to account for the uncertainty in the interpretation of the microseismic events. Uncertainty in microseismic data interpretation is also included in the algorithm (in terms of secondary probability maps) to account for the variability in the extent of the discrete fracture network within the stimulated reservoir volume (SRV). A sensitivity study is performed to understand the effect of different parameters on the well flow performance given different fracture network models. The results show that the connectivity of the fracture networks generated by the MPS method in this study is rather poor. Consequently, the permeability of the natural fractures has a dominant effect on the flow performance. In fact, the poor connectivity of fracture network does not allow to observe the effect of porosity of natural fracture and the permeability of hydraulic fracture on the flow performance. This research restresses that the MPS algorithm is not a push-a-button method to always generate reliable realizations. This work provides a guideline to better screen the generated geostatistical realization.

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/content/papers/10.3997/2214-4609.201802137
2018-09-03
2024-04-27

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References

  1. Arpat, G. B.
    [2005]. Sequential Simulation with Patterns, (January), 166.
     [Google Scholar]
  2. Behmanesh, H., Hamdi, H., & Clarkson, C. R.
    [2015]. Production data analysis of tight gas condensate reservoirs.Journal of Natural Gas Science and Engineering, 22, 22–34. https://doi.org/10.1016/J.JNGSE.2014.11.005
     [Google Scholar]
  3. Caers, J.
    [2002]. Geostatistical History Matching Under Training-Image Based Geological Model Constraints. https://doi.org/10.2118/77429-ms
  4. [2003]. History Matching Under Training-Image-Based Geological Model Constraints.SPE Journal, 8(3), 1–39. doi:http://https://doi.org/10.2118/74716-PA
     [Google Scholar]
  5. Computer Modeling Group (CMG)
    . [2017]. CMOST.
     [Google Scholar]
  6. Deutsch, C. V., & Journel, A. G.
    [1998]. GSLIB: geostatistical software library and user’s guide.Oxford University Press.
     [Google Scholar]
  7. Dickson, N. E. M., Comte, J.-C., Renard, P., Straubhaar, J. A., McKinley, J. M., & Ofterdinger, U.
    [2015]. Integrating aerial geophysical data in multiple-point statistics simulations to assist groundwater flow models.Hydrogeology Journal, 23(5), 883–900. doi:http://https://doi.org/10.1007/s10040-015-1258-x
     [Google Scholar]
  8. Fisher, M. K., Wright, C. A., Davidson, B. M., Goodwin, A. K., Fielder, E. O., Buckler, W. S., & Steinsberger, N. P.
    [2002]. Integrating Fracture Mapping Technologies to Optimize Stimulations in the Barnett Shale. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. doi:http://https://doi.org/10.2118/77441-MS
     [Google Scholar]
  9. Hamdi, H., Couckuyt, I., Sousa, M. C., & Dhaene, T.
    [2017]. Gaussian Processes for history-matching: application to an unconventional gas reservoir.Computational Geosciences. doi:http://https://doi.org/10.1007/s10596-016-9611-2
     [Google Scholar]
  10. Hamdi, H., & Sousa, M. C.
    [2016]. Calibrating Multi-Point Geostatistical Models Using Pressure Transient Data. In SPE Europec featured at 78th EAGE Conference and Exhibition. Society of Petroleum Engineers. doi:http://https://doi.org/10.2118/180163-MS
     [Google Scholar]
  11. Isaaks, E. H.
    [1990]. The application of Monte Carlo methods to the analysis of spatially correlated data/..Retrieved from https://searchworks.stanford.edu/view/709822
  12. Journel, A. G.
    [1983]. Nonparametric estimation of spatial distributions.Journal of the International Association for Mathematical Geology, 15(3), 445–468. doi:http://https://doi.org/10.1007/BF01031292
     [Google Scholar]
  13. [2002]. Combining knowledge from diverse sources: An alternative to traditional data independence hypotheses.Mathematical Geology, 34(5), 573–596. doi:http://https://doi.org/10.1023/A:1016047012594
     [Google Scholar]
  14. Kazemi, H., Merrill, L. S., Porterfield, K. L., & Zeman, P. R.
    [1976]. Numerical Simulation of Water-Oil Flow in Naturally Fractured Reservoirs. Society of Petroleum Engineers Journal, 16(06), 317–326. doi:http://https://doi.org/10.2118/5719-PA
     [Google Scholar]
  15. Khani, H., Hamdi, H., Nghiem, L., Chen, Z., & Costa Sousa, M.
    [2017]. An Improved Regional Segmentation for Probability Perturbation Method. doi:http://https://doi.org/10.3997/2214-4609.201701023
  16. Khani, H., Hamdi, H., Nghiem, L., Chen, Z., & Sousa, M. C.
    [2018]. Geologically Consistent History Matching of SAGD Process Using Probability Perturbation Method. In SPE Canada Heavy Oil Technical Conference. Society of Petroleum Engineers. doi:http://https://doi.org/10.2118/189770-MS
     [Google Scholar]
  17. Lei, Q., Latham, J.-P., & Tsang, C.-F.
    [2017]. The use of discrete fracture networks for modelling coupled geomechanical and hydrological behaviour of fractured rocks.Computers and Geotechnics, 85, 151–176. doi:http://https://doi.org/10.1016ZJ.COMPGEO.2016.12.024
     [Google Scholar]
  18. Matthäi, S. K., Mezentsev, A., & Belayneh, M.
    [2005]. Control-Volume Finite-Element Two-Phase Flow Experiments with Fractured Rock Represented by Unstructured 3D Hybrid Meshes. In SPE Reservoir Simulation Symposium. Society of Petroleum Engineers. doi:http://https://doi.org/10.2118/93341-MS
     [Google Scholar]
  19. Maxwell, S. C., Urbancic, T. I., Steinsberger, N., & Zinno, R.
    [2002]. Microseismic Imaging of Hydraulic Fracture Complexity in the Barnett Shale. In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. doi:http://https://doi.org/10.2118/77440-MS
     [Google Scholar]
  20. Remy, N., Boucher, A., & Wu, J.
    [2009]. Applied Geostatistics with SGeMS: A user’s guide. Applied Geostatistics with SGeMS: A User’s Guide. doi:http://https://doi.org/10.1017/CBO9781139150019
  21. Ren, L., Su, Y., Zhan, S., Hao, Y., Meng, F., & Sheng, G.
    [2016]. Modeling and simulation of complex fracture network propagation with SRV fracturing in unconventional shale reservoirs.Journal of Natural Gas Science and Engineering, 28, 132–141. doi:http://https://doi.org/10.1016/j.jngse.2015.11.042
     [Google Scholar]
  22. Sarda, S., Jeannin, L., Basquet, R., & Bourbiaux, B.
    [2002]. Hydraulic Characterization of Fractured Reservoirs: Simulation on Discrete Fracture Models.SPE Reservoir Evaluation & Engineering, 5(02), 154–162. doi:http://https://doi.org/10.2118/77300-PA
     [Google Scholar]
  23. Strebelle, S.
    [2002]. Onditional Simulation of Complex Geological Structures Using Multiple-Point Statistics.Mathematical Geology, 34(1), 1–21.
     [Google Scholar]
  24. [2012]. Multiple-Point Geostatistics : from Theory to Practice.Proceedings Geostats 2012, 1–9.
     [Google Scholar]
  25. Warren, J. E., & Root, P. J.
    [1963]. The Behavior of Naturally Fractured Reservoirs.Society of Petroleum Engineers Journal, 3(03), 245–255. doi:http://https://doi.org/10.2118/426-PA
     [Google Scholar]
  26. Weng, X.
    [2015]. Modeling of complex hydraulic fractures in naturally fractured formation.Journal of Unconventional Oil and Gas Resources, 9, 114–135. doi:http://https://doi.org/10.1016/JJUOGR.2014.07.001
     [Google Scholar]
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The Ability of Multiple-Point Geostatistics for Modelling Complex Fracture Networks in Tight and Shale Reservoirs
Conference Proceedings 2018, 1 (2018); https://doi.org/10.3997/2214-4609.201802137
/content/papers/10.3997/2214-4609.201802137
/content/papers/10.3997/2214-4609.201802137
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