Probabilistic Performance Forecasting for Unconventional Reservoirs with Stretched-Exponential Model
- Bunyamin Can (Texas A&M University) | C. Shah Kabir (Hess Corp.)
- Document ID
- Society of Petroleum Engineers
- North American Unconventional Gas Conference and Exhibition, 14-16 June, The Woodlands, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2011. Society of Petroleum Engineers
- 5.7.3 Deterministic Methods, 5.6.3 Deterministic Methods, 5.5.8 History Matching, 5.8.4 Shale Oil, 5.8.2 Shale Gas, 5.6.9 Production Forecasting, 3.1 Artificial Lift Systems, 5.7.2 Recovery Factors, 5.7 Reserves Evaluation, 5.7.4 Probabilistic Methods, 2 Well Completion, 3.2.3 Hydraulic Fracturing Design, Implementation and Optimisation
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Reserves estimation in an unconventional-reservoir setting is a daunting task because of geologic uncertainty, 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 with a probabilistic forecasting frame. The stretched-exponential production decline model (SEPD) underpins the production behavior. Our recovery appraisal workflow has two different applications: forecasting probabilistic future performance of wells that have production history and new wells without production data. For the new field case, numerical model runs are made in accord with the statistical design of experiments 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, etc. to focus on the decline trend. Thereafter, production data of either new or existing wells are grouped in accord with initial rates to obtain common SEPD parameters for similar wells. After determining the distribution of model parameters using well grouping, the methodology establishes a probabilistic forecast for the individual wells.
This paper presents a probabilistic performance forecasting methodology 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 parameters and alleviates the burden of having to define uncertainties associated with reservoir and well-completion parameters.
Unlike deterministic estimates, probabilistic approaches provide a measure of uncertainty in reserves estimates. For conventional systems, a plethora of publications (Damsleth et al. 1992; Friedmann et al. 2003; Kabir et al. 2004, Dehghani et al. 2008, to name a few) exist in the context of reservoir performance and/or estimating reserves with flow-simulation models. Various designs of experiments anchor the findings of these papers. However, very few studies address this issue in the context of decline-curve analysis (DCA); Cheng et. al. (2005) is a notable instance. Probabilistic methods offer a range of estimates for a prescribed confidence level, and therefore, attempt to bracket the true value. Existing practices for probabilistic estimation of reserves often assume prior knowledge of distributions of relevant parameters or reservoir properties (Cheng et al. 2005). An error in parameter distribution can lead to significantly poor estimates of reserves.
The decline-curve analysis is commonly used for future-performance prediction and reserves-estimation purposes when production data are available. These methods are used as a deterministic tool and, in most cases, yield a single deterministic estimate. Such an estimate can deviate significantly from the actual production trend, particularly in an unconventional setting. The applicability of decline-curve analysis to probabilistic forecasting is an obvious choice as production maturity occurs. In the context of conventional assets, Wolff (2010) observed that the industry has begun to move from single-valued assessments because strong subsurface uncertainties dictate an ongoing consideration of range of outcomes. Another motivation for the probabilistic DCA approach in unconventional assets is that uncertainties arising from completion and the resultant flow variables make quantifying the variables challenging, in addition to subsurface uncertainties.
|File Size||9 MB||Number of Pages||12|