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Integration of Shale-Gas-Production Data and Microseismic for Fracture and Reservoir Properties With the Fast Marching Method
- Jiang Xie (Texas A&M University) | Changdong Yang (Texas A&M University) | Neha Gupta (Texas A&M University) | Michael J. King (Texas A&M University) | Akhil Datta-Gupta (Texas A&M University)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- July 2014
- Document Type
- Journal Paper
- 2014.Society of Petroleum Engineers
- 6.5.8 History Matching, 6 Reservoir Description and Dynamics, 6.5.6 Dynamic Model Update Algorithms, 6.9 Unconventional Hydrocarbon Recovery, 6.5 Reservoir Simulation, 6.9.2 Shale Gas
- Microseismic data , Unconventional reservoirs , Fast Marching Method, History Matching
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- 279 since 2007
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We present a novel approach to calculate drainage volume and well performance in shale gas reservoirs by use of the fast marching method (FMM) combined with a geometric pressure approximation. Our approach can fully account for complex fracture-network geometries associated with multistage hydraulic fractures and their impact on the well pressure and rates. The major advantages of our proposed approach are its simplicity, intuitive appeal, and computational efficiency. For example, we can compute and visualize the time evolution of the well-drainage volume for multimillion-cell geologic models in seconds without resorting to reservoir simulation. A geometric approximation of the drainage volume is then used to compute the well rates and the reservoir pressure. The speed and versatility of our proposed approach make it ideally suited for parameter estimation by means of the inverse modeling of shale-gas performance data. We use experimental design to perform the sensitivity analysis to identify the "heavy hitters" and a genetic algorithm (GA) to calibrate the relevant fracture and matrix parameters in shale-gas reservoirs by history matching of production data. In addition to the production data, microseismic information is used to help us constrain the fracture extent and orientation and to estimate the stimulated reservoir volume (SRV). The proposed approach is applied to a fractured shale-gas well. The results clearly show reduced ranges in the estimated fracture parameters and SRV, leading to improved forecasting and reserve estimation.
Albright, J.N. and Pearson, C.F. 1982. Acoustic Emissions as a Tool for Hydraulic Fracture Location: Experience at the Fenton Hill Hot Dry Rock Site. SPE J. 22 (4): 523–530. SPE-9509-PA. http://dx.doi.org/10.2118/9509-PA.
Al-Harbi, M., Cheng, H., He, Z. et al. 2005. Streamline-Based Production Data Integration in Naturally Fractured Reservoirs. SPE J. 10 (4): 426–439.
Bittencourt, A.C. and Horne, R.N. 1997. Reservoir Development and Design Optimization. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 5–8 October. SPE-38895-MS. http://dx.doi.org/10.2118/38895-MS.
Cheng, H., Dehghani, K., and Billiter, T. 2008. A Structured Approach for Probabilistic-Assisted History Matching Using Evolutionary Algorithms: Tengiz Field Applications. Presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, 21–24 September. SPE-116212-MS. http://dx.doi.org/10.2118/116212-MS.
Cipolla, C.L., Lolon, E.P., Erdle, J.C. et al. 2009. Modeling Well Performance in Shale-Gas Reservoirs. Presented at the SPE/EAGE Reservoir Characterization and Simulation Conference, Abu Dhabi, UAE, 19–21 October. SPE-125532-MS. http://dx.doi.org/10.2118/125532-MS.
Cipolla, C.L., Fitzpatrick, T., Williams, M.J. et al. 2011a. Seismic-to-Simulation for Unconventional Reservoir Development. Presented at the SPE Reservoir Characterization and Simulation Conference, Abu Dhabi, UAE, 9–11 October. SPE-146876-MS. http://dx.doi.org/10.2118/146876-MS.
Cipolla, C.L., Weng, X., Mack, M.G. et al. 2011b. Integrating Microseismic Mapping and Complex Fracture Modeling to Characterize Hydraulic Fracture Complexity. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, 24–26 January. SPE-140185-MS. http://dx.doi.org/10.2118/140185-MS.
Cipolla, C.L., Maxwell, S., Mack, M.G. et al. 2012. Engineering Guide to the Application of Microseismic Interpretations. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, 6–8 February. SPE-152165-MS. http://dx.doi.org/10.2118/152165-MS.
Clarkson, C.R., Nobakht, M., Kaviani, D. et al. 2012. Production Analysis of Tight-Gas and Shale-Gas Reservoirs Using the Dynamic-Slippage Concept. SPE J. 17 (1): 230–242. SPE-144317-PA. http://dx.doi.org/10.2118.144317-PA.
Cramer, D.D. 2008. Stimulating Unconventional Reservoirs: Lesson Learned, Successful Practices, Area for Improvement. Presented at the SPE Unconventional Reservoirs Conference, Keystone, Colorado, 10–12 February. SPE-114172-MS. http://dx.doi.org/10.2118/114172-MS.
Datta-Gupta, A., Xie, J., Gupta, N. et al. 2011. Radius of Investigation and Its Generalization to Unconventional Reservoirs. J Pet Technol 63 (7): 52–55.
Fan, L., Thompson, J.W., and Robinson, J.R. 2010. Understanding Gas Production Mechanism and Effectiveness of Well Stimulation in the Haynesville Shale Through Reservoir Simulation. Presented at the Canadian Unconventional Resources and International Petroleum Conference, Calgary, Alberta, Canada, 19–21 October. SPE-136696-MS. http://dx.doi.org/10.2118/136696-MS.
Fetkovich, M.J. 1980. Decline Curve Analysis Using Type Curves. J Pet Technol 32 (6): 1065–1077.
Fisher, M.K., Wright, C.A., Davidson, B.M. et al. 2005. Integrating Fracture-Mapping Technologies to Improve Stimulations in the Barnett Shale. SPE Prod & Fac 20 (2): 85–93. SPE-77441-PA. http://dx.doi.org/10.2118/77441-PA.
Floris, F.J.T., Bush, M.D., Cuypers, M. et al. 2001. Methods for Quantifying the Uncertainty of Production Forecast: A Comparative Study. Petroleum Geosci. 7 (S): S87–S96.
Ghods, P. and Zhang, D. 2010. Ensemble-Based Characterization and History Matching of Naturally Fractured Tight/Shale Gas Reservoirs. Presented at the SPE Western Regional Meeting, Anaheim, California, 27–29 May. SPE-133606-MS. http://dx.doi.org/10.2118/133606-MS.
Holditch, S.A. 2010. Shale Gas Holds Global Opportunities. The American Oil & Gas Reporter, August 2010 Editor’s Choice.
Ilk, D., Stotts, G.W.J., Anderson, D.M. et al. 2010. Production Data Analysis—Challenges, Pitfalls, Diagnostics. SPE Res Eval & Eng 13 (3): 538–552. SPE-102048-PA. http://dx.doi.org/10.2118/102048-PA.
Iman, R.L., Davenport, J.M., and Zeigler, D.K. 1980. Latin Hypercube Sampling (Program User’s Guide). [LHC, in FORTRAN]
Kim, J.U., Datta-Gupta, A., Brouwer, R. et al. 2009. Calibration of High-Resolution Reservoir Models Using Transient Pressure Data. Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, 4–7 October. SPE-124834-MS. http://dx.doi.org/10.2118/124834-MS.
King, G.E. 2010. Thirty Years of Gas Shale Fracturing: What Have We Learned? Presented at the SPE Annual Technical Conference and Exhibition, Florence, Italy, 19–22 September. SPE-133456-MS. http://dx.doi.org/10.2118/133456-MS.
Kulkarni, K.N., Datta-Gupta, A., and Vasco, D.W. 2000. A Streamline Approach for Integrating Transient Pressure Data Into High Resolution Reservoir Models. Presented at the SPE European Petroleum Conference, Paris, France, 24–25 October. SPE-65120-MS. http://dx.doi.org/10.2118/65120-MS.
Lee, W.J. 1982. Well Testing. SPE Textbook Series. Richardson, Texas: Society of Petroleum Engineers.
Lee, W.J., Rollins, J.B., and Spivey, J.P. 2003. Pressure-Transient Testing. SPE Textbook Series. Richardson, Texas: Society of Petroleum Engineers.
Maxwell, S.C., Urbancic, T.I., Steinsberger, N. et al. 2002. Microseismic Imaging of Hydraulic Fracture Complexity in the Barnett Shale. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 29 September–2 October. SPE-77440-MS. http://dx.doi.org/10.2118/77440-MS.
Mayerhofer, M.J., Lolon, E.P., Warpinski, N.R. et al. 2010. What Is Stimulated Reservoir Volume? SPE Prod & Oper 25 (1): 89–98. SPE-119890-PA. http://dx.doi.org/10.2118/119890-PA.
Mayerhofer, M.J., Lolon, E.P., Youngblood, J.E. et al. 2006. Integration of Microseismic-Fracture-Mapping Results With Numerical Fracture Network Production Modeling in Barnett Shale. Presented at the SPE Annual Technical Conference and Exhibition, San Antonio, Texas, 24–27 September. SPE-102103-MS. http://dx.doi.org/10.2118/102103-MS.
Nordbotten, J.M., Celia, M.A., and Bachu, S. 2004. Analytical Solutions for Leakage Rates Through Abandoned Wells. Water Resources Res. 40: W04204.
Romero, C.E. and Carter, J.N. 2001. Using Genetic Algorithm for Reservoir Characterization. J. Petrol. Sci. & Eng. 31 (2–4): 113–123.
Rutledge, J.T. and Philips, W.S. 2003. Hydraulic Stimulation of Natural Fractures as Revealed by Induced Microearthquakes, Carthage Cotton Valley Gas Field, East Texas. Geophysics 68 (2): 441.
Schulze-Riegert, R.W., Axmann, J.K., Haase, O. et al. 2002. Evolutionary Algorithms Applied to History Matching of Complex Reservoirs. SPE Res Eval & Eng 5 (2): 163–173. SPE-77301-PA. http://dx.doi.org/10.2118/77301-PA.
Schulze-Riegert, R.W., Haase, O., and Nekrassov, A. 2003. Combined Global and Local Optimization Techniques Applied to History Matching. Presented at the SPE Reservoir Simulation Symposium, Houston, Texas, 3–5 February. SPE-79668-MS. http://dx.doi.org/10.2118/79668-MS.
Sethian, J.A. 1999a. Fast Marching Method. SIAM Rev. 41 (2): 199–235.
Sethian, J.A. 1999b. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Material Science. Series: Cambridge Monographs on Applied and Computational Mathematics; 3. Cambridge, UK: Cambridge University Press.
Sethian, J.A. and Vladimirsky, A. 2000. Fast Methods for the Eikonal and Related Hamilton-Jacobi Equations on Unstructured Meshes. Proc. Natl. Acad. Sci. USA 97 (11): 5699–5703.
Song, B. and Ehlig-Economides, C.A. 2011. Rate-Normalized Pressure Analysis for Determination of Shale Gas Well Performance. Presented at the North American Unconventional Gas Conference and Exhibition, The Woodlands, Texas, 14–16 June. SPE-144031-MS. http://dx.doi.org/10.2118/144031-MS.
Valko, P.P. and Lee, W.J. 2010. A Better Way To Forecast Production From Unconventional Gas Wells. Presented at the SPE Annual Technical Conference and Exhibition, Florence, Italy, 19–22 September. SPE-134231-MS. http://dx.doi.org/10.2118/134231-MS.
Vasco, D.W., Keers, H., and Karasaki, K. 2000. Estimation of Reservoir Properties Using Transient Pressure Data: An Asymptotic Approach. Water Resources Res. 36 (12): 3447–3465.
Warpinski, N.R., Wolhart, S.L., and Wright, C.A. 2004. Analysis and Prediction of Microseismicity Induced by Hydraulic Fracturing. SPE J. 9 (1): 24–33. SPE-87673-PA. http://dx.doi.org/10.2118/87673-PA.
Williams, G.J.J., Mansfield, M., MacDonald, D.G. et al. 2004. Top-Down Reservoir Modelling. Presented at the SPE Annual Technical Conference and Exhibition, Houston, Texas, 26–29 September. SPE-89974-MS. http://dx.doi.org/10.2118/89974-MS.
Winestock, A.G. and Colpitts, G.P. 1965. Advances in Estimating Gas Well Deliverability. J Can Pet Technol 4 (3): 111–119.
Xie, J., Gupta, N., King, M.J. et al. 2012. Depth of Investigation and Depletion Behavior in Unconventional Reservoirs Using Fast Marching Methods. Presented at the EAGE Annual Conference and Exhibition Incorporating SPE Europec, Copenhagen, Denmark, 4–7 June.
Xu, W., Thiercelin, M., and Walton, I. 2009. Characterization of Hydraulically Induced Shale Fracture Network Using a Semi-Analytical Model. Presented at the Annual Technical Conference and Exhibition, New Orleans, Louisiana, 4–7 October. SPE-124697-MS. http://dx.doi.org/10.2118/124697-MS.
Yeten, B., Castellini, A., Guyaguler, B. et al. 2005. A Comparison Study on Experimental Design and Response Surface Methodologies. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 31 January–2 February. SPE-93347-MS. http://dx.doi.org/10.2118/93347-MS.
Yin, J., Park, H., Datta-Gupta, A. et al. 2010. A Hierarchical Streamline-Assisted History Matching Approach With Global and Local Parameter Updates. Presented at the SPE Western Regional Meeting, Anaheim, California, 27–29 May. SPE-132642-MS. http://dx.doi.org/10.2118/132642-MS.
Yin, J., Xie, J., Datta-Gupta, A. et al. 2011. Improved Characterization and Performance Assessment of Shale Gas Wells by Integrating Stimulated Reservoir Volume and Production Data. Presented at the SPE Eastern Region Meeting, Columbus, Ohio, 17–19 August. SPE-148969-MS. http://dx.doi.org/10.2118/148969-MS.
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