Summary 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.

Empirical and/or semianalytical tools are frequently applied in most waterflood operations, although grid-based models are also often used. This paper examines the performance of some commonly used tools, such as the water-oil ratio (WOR), Y-function, and Arps. Besides those tools, we introduce a semianalytical approach, which is a modified version of the Y-function formulation. Two other tools that have gained significant traction in unconventional-reservoir performance forecasting, the stretched-exponential decline model (SEDM) and the capacitance-resistance model (CRM), are also used here. Based on synthetic and field data, the results show that the Arps method is remarkably accurate in all flooding situations, regardless of the underlying physical mechanisms; other published data tend to support this notion. Similarly, both the SEDM and the proposed modified-Y-function method also yield solutions with good accuracy. The latter solutions tend to be pessimistic, however.

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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 reservesevaluation 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 forecasting production from 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 iv model parameters using well grouping, the methodology establishes a probabilistic forecast for individual wells.We present 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.

No abstract