Probabilistic Production Forecasting for Unconventional Reservoirs With Stretched Exponential Production Decline Model
- Bunyamin Can (Texas A&M University) | Shah Kabir (Hess Corporation)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- February 2012
- Document Type
- Journal Paper
- 41 - 50
- 2012. Society of Petroleum Engineers
- 2 Well Completion, 5.7 Reserves Evaluation, 3.1 Artificial Lift Systems, 5.6.9 Production Forecasting
- Probabilistic forecasting, Estimated ultimate recovery, Unconventional reservoirs, EUR
- 7 in the last 30 days
- 2,194 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
Reserves estimation in an unconventional-reservoir setting is a daunting task because of geologic uncertainty and complex flow patterns evolving in a long, stimulated horizontal well, among other variables. To tackle this complex problem, we present a reserves-evaluation workflow that couples the traditional decline-curve analysis (DCA) with a probabilistic forecasting frame. The stretched-exponential production-decline (SEPD) model underpins the production behavior. Our recovery appraisal workflow has two different applications: forecasting probabilistic future performance of (1) wells that have production history and of (2) new wells without production data. For the new-field case, numerical-model runs are made in accord with the statistical design of experiments (DOE) for a range of design variables pertinent to the field of interest. In contrast, for the producing wells, the early-time data often need adjustments owing to restimulation, installation of artificial lift, or other factors to focus on the decline trend. Thereafter, production data of either new or existing wells are grouped in accordance with maximum rates to obtain common SEPD model parameters for similar wells. After determining the distribution of model parameters using the well-grouping approach, the method establishes a probabilistic forecast for the individual wells.
This paper presents a probabilistic performance-forecasting method in unconventional reservoirs for wells with and without production history. Unlike other probabilistic forecasting tools, grouping wells with similar production character allows estimation of self-consistent SEPD-model parameters and alleviates the burden of having to define uncertainties associated with reservoir and well-completion parameters.
|File Size||2 MB||Number of Pages||10|
Arps, J.J. 1956. Estimation of Primary Oil Reserves. In Transactions ofthe American Institute of Mining, Metallurgical, and Petroleum Engineers,ed. Vol. 2007, 182-191. New York City: AIME Petroleum Branch.
Cheng, Y., Wang, Y., McVay, D.A., and Lee, W.J. 2005. Practical Applicationof a Probabilistic Approach to Estimate Reservoirs Using Production DeclineData. Paper SPE 95974 presented at the SPE Annual Technical Conference andExhibition, Dallas, 9-12 October. http://dx.doi.org/10.2118/95974-MS.
Damsleth, E., Hage, A., and Volden, R. 1992. Maximum Information at MinimumCost: A North Sea Field Development Study Using Experimental Design. J PetTechnol 44 (12): 1350-1356. SPE-23139-PA. http://dx.doi.org/10.2118/23139-PA.
Dehghani, K., Fischer, D.J., Fischer, D., and Skalinski, M. 2008.Application of Integrated Reservoir Studies and Techniques To Estimate OilVolumes and Recovery—Tengiz Field, Republic of Kazakhstan. SPE Res Eval& Eng 11 (2): 362-378. SPE-102197-PA. http://dx.doi.org/10.2118/102197-PA.
Friedmann, F., Chawathé, A., and Larue, D.K. 2003. Assessing Uncertainty inChannelized Reservoirs Using Experimental Designs. SPE Res Eval &Eng 6 (4): 264-274. SPE-85117-PA. http://dx.doi.org/10.2118/85117-PA.
Hefner, J.M. and Thompson, R.S. 1996. A Comparison of Probabilistic andDeterministic Reserve Estimates: A Case Study. SPE Res Eng 11 (1): 43-47. SPE-26388-PA. http://dx.doi.org/10.2118/26388-PA.
Ilk, D., Anderson, D.M., Stotts, G.W.J., Mattar, L., and Blasingame, T.A.2010. Production Data Analysis--Challenges, Pitfalls, Diagnostics. SPE ResEval & Eng 13 (3): 538-552. SPE-102048-PA. http://dx.doi.org/10.2118/102048-PA.
Ilk, D., Rushing, J.A., Perego, A.D., and Blasingame, T.A. 2008. Exponentialvs. Hyperbolic Decline in Tight Gas Sands—Understanding the Origin andImplications for Reserve Estimates Using Arps' Decline Curves. Paper SPE 116731presented at the SPE Annual Technical Conference and Exhibition, Denver. http://dx.doi.org/10.2118/116731-MS.
Jochen, V.A. and Spivey, J.P. 1996. Probabilistic Reserves Estimation UsingDecline Curve Analysis with the Bootstrap Method. Paper SPE 36633 presented atthe SPE Annual Technical Conference and Exhibition, Denver, 6-9 October. http://dx.doi.org/10.2118/36633-MS.
Kabir, C.S. and Lake, L.W. 2011. An Analytical Approach to Estimating EUR inUnconventional Reservoirs. Paper SPE 144311 presented at the North AmericanUnconventional Gas Conference and Exhibition, The Woodlands, Texas, USA, 14-16June. http://dx.doi.org/10.2118/144311-MS.
Kabir, C.S., Chawathé, A., Jenkins, S.D., Olayomi, A.J., Aigbe, C., andFaparusi, D.B. 2004. Developing New Fields Using Probabilistic ReservoirForecasting. SPE Res Eval & Eng 7 (1): 15-23. SPE-87643-PA.http://dx.doi.org/10.2118/87643-PA.
Kabir, S., Rasdi, F., and Igboalisi, B. 2011. Analyzing Production Data FromTight Oil Wells. J Can Pet Technol 50 (5): 48-58.SPE-137414-PA. http://dx.doi.org/10.2118/137414-PA.
Valkó, P.P. 2009. Assigning Value to Stimulation in the Barnett Shale: ASimultaneous Analysis of 7000 Plus Production Histories and Well CompletionRecords. Paper SPE 119369 presented at the SPE Hydraulic Fracturing TechnologyConference, The Woodlands, Texas, USA, 19-21 January. http://dx.doi.org/10.2118/119369-MS.
Valkó, P.P. and Lee, W.J. 2010. A Better Way To Forecast Production FromUnconventional Gas Wells. Paper SPE 134231 presented at the SPE AnnualTechnical Conference and Exhibition, Florence, Italy, 19-22 September. http://dx.doi.org/10.2118/134231-MS.
Wolff, M. 2010. Probabilistic Subsurface Forecasting—What Do We Really Know?J Pet Technol 62 (5): 86-92. SPE-118550-MS.http://dx.doi.org/10.2118/118550-MS.