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
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- 1,966 since 2007
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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.
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