Combining Decline-Curve Analysis and Geostatistics To Forecast Gas Production in the Marcellus Shale
- Zhenke Xi (Pennsylvania State University) | Eugene Morgan (Pennsylvania State University)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- May 2019
- Document Type
- Journal Paper
- 2019.Society of Petroleum Engineers
- linear regression, unconventional play prediction, data analytics, decline curve
- 50 in the last 30 days
- 138 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
To estimate the production potential at a new, prospective field site by means of simulation or material balance, one needs to collect various forms of costly field data and make assumptions about the nature of the formation at that site. Decline-curve analysis (DCA) would not be applicable in this scenario, because producing wells need to pre-exist in the target field. The objective of our work was to make first-order forecasts of production rates at prospective, undrilled sites using only production data from existing wells in the entire play. This is accomplished through the co-Kriging of decline-curve parameter values, where the parameter values are obtained at each existing well by fitting an appropriate decline model to the production history. Co-Kriging gives the best linear unbiased prediction of parameter values at undrilled locations, and also estimates uncertainty in those predictions. Thus, we obtained production forecasts at P10, P50, and P90, and we calculated the estimated ultimate recovery (EUR) at those same levels across the spatial domain of the play.
To demonstrate the proposed methodology, we used monthly gas-flow rates and well locations from the Marcellus shale-gas play in this research. Monitoring only horizontal and directional wells, the gas-production rates at each well were carefully filtered and screened. Also, we normalized the rates by perforation-interval length. We only kept production histories of 24 months or longer to ensure good decline-curve fits. Ultimately, we were left with 5,637 production records. Here, we chose Duong’s decline model (Duong 2011) to represent the production decline in this shale-gas play, and fitting this decline curve was accomplished through ordinary least-squares (OLS) regression.
Interpolation was done by universal co-Kriging while considering the correlation between the four parameters in Duong’s model, which also showed linear trends (the parameters showed dependency on the x and y spatial coordinates). Kriging gave us the optimal decline-curve coefficients at new locations (P50 curve), as well as the variance in these coefficient estimates (used to establish P10 and P90 curves). We were also able to map EUR for 25 years across the study area. Finally, the universal co-Kriging model was cross validated with a leave-one-out scheme, which showed significant, but not unreasonable, error in the decline-curve-coefficient prediction. The methods proposed were implemented and did not require various costly data, such as permeability and bottomhole pressure, thus giving operators a risk-based analysis of prospective sites. While we demonstrated the procedure on the Marcellus shale-gas play, it is applicable to any play with existing producing wells. We also made this analysis available to the public in a user-friendly web application (Xi and Morgan 2018).
|File Size||2 MB||Number of Pages||13|
Anderson, D. M. and Liang, P. 2011. Quantifying Uncertainty in Rate Transient Analysis for Unconventional Gas Reservoirs. Presented at the North American Unconventional Gas Conference and Exhibition, The Woodlands, Texas, 14–16 June. SPE-145088-MS. https://doi.org/10.2118/145088-MS.
Arps, J. J. 1945. Analysis of Decline Curves. Trans Metall Soc AIME 160: 228–247. https://doi.org/10.2118/945228-G.
Bhattacharya, S. and Nikolaou, M. 2013. Analysis of Production History for Unconventional Gas Reservoirs With Statistical Methods. SPE J. 18 (5): 878–896. SPE-147658-PA. https://doi.org/10.2118/147658-PA.
Cipolla, C. L., Lolon, E. P., Erdle, J. C. et al. 2010. Reservoir Modeling in Shale-Gas Reservoirs. SPE Res Eval & Eng 13 (4): 638–653. SPE-125530-PA. https://doi.org/10.2118/125530-PA.
Clarkson, C. R., Jensen, J. L., and Chipperfield, S. 2012. Unconventional Gas Reservoir Evaluation: What Do We Have To Consider? J Nat Gas Sci Eng 8: 9–33. https://doi.org/10.1016/j.jngse.2012.01.001.
Diaz De Souza, O. C., Sharp, A., Martinez, R. C. et al. 2012. Integrated Unconventional Shale Gas Reservoir Modeling: A Worked Example From the Haynesville Shale, De Soto Parish, North Louisiana. Presented at the SPE Americas Unconventional Resources Conference, Pittsburgh, Pennsylvania, 5–7 June. SPE-154692-MS. https://doi.org/10.2118/154692-MS.
Diggle, P. and Ribeiro, P. J. 2007. Model-Based Geostatistics. New York: Springer-Verlag. https://doi.org/10.1007/978-0-387-48536-2.
DrillingInfo. 2018. DrillingInfo. https://info.drillinginfo.com (accessed 2 May 2018).
EIA (Energy Information Administration). 2017. Marcellus Shale Play: Geology Review, Washington, DC: US Department of Energy, https://www.eia.gov/maps/pdf/MarcellusPlayUpdate_Jan2017.pdf (accessed 15 August 2018).
Erenpreiss, M., Wickstrom, L., Riley, R. A. et al. 2012. Mapping the Regional Organic Thickness of the “Marcellus Shale” Hamilton Group. Presented at the Geological Society of America 46th Annual Meeting, Dayton, Ohio, 23–24 April.
Erdle, J., Hale, B., Houze, O. et al. 2016. Monograph 4: Estimating Ultimate Recovery of Developed Wells in Low-Permeability Reservoirs. Houston: Society of Petroleum Evaluation Engineers.
Hauge, V. L. and Hermansen, G. H. 2017. Machine Learning Methods for Sweet Spot Detection: A Case Study. In Geostatistics Valencia 2016, ed. J. J. Gómez-Hernández, J. Rodrigo-Ilarri, M. E. Rodrigo-Clavero, E. Cassiraga, and J. A. Vargas-Guzmán, Chap. 19, 573–588. Cham, Switzerland: Springer International Publishing. https://doi.org/10.1007/978-3-319-46819-8_38.
Joshi, K. and Lee, J. 2013. Comparison of Various Deterministic Forecasting Techniques in Shale Gas Reservoirs. Presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, 4–6 February. SPE-163870-MS. https://doi.org/10.2118/163870-MS.
Journel, A. G. and Huijbregts, C. J. 1978. Mining Geostatistics. London: Academic Press.
Ketineni, S. P., Ertekin, T., Anbarci, K. et al. 2015. Structuring an Integrative Approach for Field Development Planning Using Artificial Intelligence and Its Application to an Offshore Oilfield. Presented at the SPE Annual Technical Conference and Exhibition, Houston, 28–30 September. SPE-174871-MS. https://doi.org/10.2118/174871-MS.
Male, F., Marder, M. P., Browning, J. et al. 2016. Marcellus Wells’ Ultimate Production Accurately Predicted From Initial Production. Presented at the SPE Low Perm Symposium, Denver, 5–6 May. SPE-180234-MS. https://doi.org/10.2118/180234-MS.
Mallick, M. and Achalpurkar, M. P. 2014. Factors Controlling Shale Gas Production: Geological Perspective. Presented at the Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 10–13 November. SPE-171823-MS. https://doi.org/10.2118/171823-MS.
Matheron, G. 1971. The Theory of Regionalised Variables and Its Applications. Paris: Les Cahiers du Centre de Morphologie Mathématique.
McBratney, A. B. and Webster, R. 1983. Optimal Interpolation and Isarithmic Mapping of Soil Properties: V. Co-Regionalization and Multiple Sampling Strategy. Eur J Soil Sci 34 (1): 137–162. https://doi.org/10.1111/j.1365-2389.1983.tb00820.x.
Moinfar, A., Varavei, A., Sepehrnoori, K. et al. 2013. Development of a Coupled Dual Continuum and Discrete Fracture Model for the Simulation of Unconventional Reservoirs. Presented at the SPE Reservoir Simulation Symposium, The Woodlands, Texas, 18–20 February. SPE-163647-MS. https://doi.org/10.2118/163647-MS.
Vauclin, M., Vieira, S. R., Vachaud, G. et al. 1983. The Use of Cokriging With Limited Field Soil Observations 1. Soil Sci Soc Am J 47 (2): 175–184. https://doi.org/10.2136/sssaj1983.03615995004700020001x.
Wang, G. and Carr, T. R. 2013. Organic-Rich Marcellus Shale Lithofacies Modeling and Distribution Pattern Analysis in the Appalachian Basin. AAPG Bull 97 (12): 2173–2205. https://doi.org/10.1306/05141312135.
West Virginia Geologic and Economic Survey (WVGES). 2018. WVGES Marcellus Wells. http://www.wvgs.wvnet.edu/www/datastat/Marcellus/Downloads/WVGES%20Marcellus%20Wells.xlsx (accessed 7 May 2018).
Wrightstone, G. R. 2009. Marcellus Shale—Geologic Controls on Production. Search Discov 10206: 1–10.
Xi, Z. and Morgan, E. 2018. Virtual Asset 1.0: Marcellus Shale. https://shinysrv.ems.psu.edu/eum19/Virtual_Asset_1_0/ (accessed 13 May 2019).
Zou, C., Dong, D., Wang, S. et al. 2010. Geological Characteristics and Resource Potential of Shale Gas in China. Pet Explor Dev 37 (6): 641–653. https://doi.org/10.1016/S1876-3804(11)60001-3.