Abstract
Over the years, productivity studies conducted for horizontal multistage completions have shown significant stage-wise variability. Optimizing such completions could hold the key to unlocking true value from shale reservoirs and improving well economics. Traditional hydraulic fracturing programs use the same fracture design along laterals without any consideration for changes in the reservoir and wellbore conditions. Methods using mechanical rock properties require expensive petrophyisical logging data, while those involving use of drill cuttings can be highly resource intensive and time consuming. In this paper, we introduce a novel approach, which utilizes routinely available data such as measurements made while drilling and petrophyisical data as available within a fracture spacing design framework. We validate our approach through application on multiple wells by comparing results from our workflow with post completion production logging. Finally, we highlight the potential advantages and pitfalls in our approach and present a roadmap for future implementation in different plays.
References
1. J.W. Crafton and D. Anderson, Use of extremely high time-resolution production data to characterize hydraulic fracture properties, Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24–27 September, SPE-103591-MS, http://dx.doi.org/10.2118/103591-MS (2006).10.2118/103591-MSSearch in Google Scholar
2. P. Ye, L. Chu, I. Harmawan, et al., Beyond linear flow analysis in an unconventional reservoir. Presented at the SPE Unconventional Resources Conference, The Woodlands, Texas, 10–12 April, SPE-164543-MS, http://dx.doi.org/10.2118/164543-MS (2013).10.2118/164543-MSSearch in Google Scholar
3. K. Wu and J.E. Olson, Investigation of the impact of fracture spacing and fluid properties for interfering simultaneously or sequentially generated hydraulic fractures. SPE Production & Operations28(4), 427–436 (2013).10.2118/163821-PASearch in Google Scholar
4. M. Jonathan and J.L. Miskimins, Optimization of hydraulic fracture spacing in unconventional shales. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, 6–8 February, SPE-152595-MS, http://dx.doi.org/10.2118/152595-MS (2012).10.2118/152595-MSSearch in Google Scholar
5. C. Cipolla and J. Wallace, Stimulated reservoir volume: A misapplied concept? Hydraulic Fracturing Journal1(2), 54–75 (2014).10.2118/168596-MSSearch in Google Scholar
6. X. Ma, T. Plaksina, and E. Gildin, Optimization of placement of hydraulic fracture stages in horizontal wells drilled in shale gas reservoirs, Presented at the Unconventional Resources Technology Conference, Denver, Colorado, 12–14 August, SPE-168790-MS, http://dx.doi.org/10.1190/URTEC2013-151 (2013).10.1190/urtec2013-151Search in Google Scholar
7. A. Baig, T.I. Urbancic, K.C. Mace, et al. Assessing the spacing of stages in plug and Perf completions through seismic moment tensor inversion, Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, 6–8 February, SPE-152547-MS, http://dx.doi.org/10.2118/152547-MS (2012).10.2118/152547-MSSearch in Google Scholar
8. L.W. Bazan, S.D. Larkin, M.G. Lattibeaudiere, et al., Improving production in the Eagle Ford Shale with fracture modeling, increased fracture conductivity, and optimized stage and cluster spacing along the horizontal wellbore, Presented at the Tight Gas Completions Conference, San Antonio, Texas, 2–3 November, SPE-138425-MS, http://dx.doi.org/10.2118/138425-MS (2010).10.2118/138425-MSSearch in Google Scholar
9. R.L. Kennedy, R. Gupta, S.V. Kotov, et al., Optimized shale resource development: Proper placement of wells and hydraulic fracture stages, Presented at the Abu Dhabi International Petroleum Conference and Exhibition, Abu Dhabi, UAE, 11–14 November, SPE-162534-MS, http://dx.doi.org/10.2118/162534-MS (2012).10.2118/162534-MSSearch in Google Scholar
10. J. Ciezobka, Marcellus shale gas project, RPSEA, August 2013, http://www.rpsea.org/files/3629/ (accessed 11 May 2015) (2013).Search in Google Scholar
11. B. Ajayi, I.I. Aso, I.J. Terry, et al. Simulation design for unconventional resources. Oilfield Review25(2), 34–46 (2013).Search in Google Scholar
12. N.R. Warpinski, M.J. Mayerhofer, E.J. Davis, et al., Integrating fracture diagnostics for improved microseismic interpretation and stimulation modeling, Presented at the Unconventional Resources Technology Conference, Denver, Colorado, 25–27 August, SPE-2014-1917906-MS, http://dx.doi.org/10.15530/urtec-2014-1917906 (2014).10.15530/urtec-2014-1917906Search in Google Scholar
13. R. Borstmayer, N. Stegent, A. Wagner, et al., Approach optimizes completion design. The American Oil & Gas Reporter 54 (8), 79–88 (2011).Search in Google Scholar
14. A. Jahandideh and B. Jafarpour, Optimization of hydraulic fracturing design under spatially variable shale fracability, Presented at the SPE Western North American and Rocky Mountain Joint Meeting, Denver, Colorado, 17–18 April, SPE-169521-MS, http://dx.doi.org/10.2118/169521-MS (2014).10.2118/169521-MSSearch in Google Scholar
15. T. Engelder, G.G. Lash, and R.S. Uzcategui, Joint sets that enhance production from Middle and Upper Devonian gas shales of the Appalachian Basin. AAPG Bulletin93(7), 857–889 (2009).10.1306/03230908032Search in Google Scholar
16. B. Bahorich, J.E. Olson, and J. Holder, Examining the effect of cemented natural fractures on hydraulic fracture propagation in hydrostone block experiments. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 8–10 October, SPE-160197-MS, http://dx.doi.org/10.2118/160197-MS (2012).10.2118/160197-MSSearch in Google Scholar
17. B. Gutenberg and C.F. Richter, Seismicity of the Earth and Associated Phenomena, second edition,. Princeton University Press, Princeton, NJ (1954).Search in Google Scholar
18. N. Boroumand, Hydraulic fracture b-value from microseismic events in different regions. Presented at GeoConvention, Calgary, Canada, 12–16 May (2014).Search in Google Scholar
19. I.A. Salehi and J. Ciezobka, Controlled hydraulic fracturing of naturally fractured shales: A case study in the marcellus shale examining how to identify and exploit natural fractures. Presented at the SPE Unconventional Resources Conference, The Woodlands, Texas, 10–12 April, SPE-164524-MS, http://dx.doi.org/10.2118/164524-MS (2013).10.2118/164524-MSSearch in Google Scholar
20. R. Rickman, M.J. Mullen, J.E. Petre, et al., A practical use of shale petrophysics for stimulation design optimization: All shale plays are not clones of the Barnett shale, Presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, 21–24 September, SPE-115258-MS, http://dx.doi.org/10.2118/115258-MS (2008).10.2118/115258-MSSearch in Google Scholar
21. S. Haykin, Neural Networks: A Comprehensive Foundation, second edition, Prentice Hall, Saddle River, NJ (1998).Search in Google Scholar
22. T.J. Ross, Fuzzy Logic with Engineering Applications, third edition, John Wiley & Sons, West Sussex, UK (2009).Search in Google Scholar
23. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, first edition, Addison-Wesley Longman, Boston, MA: (1989).Search in Google Scholar
© 2016 D. Maity et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
