Abstract
Risk or uncertainty assessment in the reservoir flow modeling, especially in real field-scale evaluation, is essential to make a trustful decision regarding the future development plans. This paper presents an efficient uncertainty assessment workflow of geological and production data through the cyclic text{CO}_2-assisted gravity drainage (GAGD) process in South Rumaila oil field in Southern Iraq. First, the sequential Gaussian simulation created a large number of reservoir stochastic realizations that capture the entire geological uncertainty space. Second, ranking was applied to select the quartiles (text{P}10, text{P}20,ldots ,text{P}90) of reservoir permeability and anisotropy ratio to quantify the geological uncertainty. Next, the equation of state-compositional flow model was constructed to evaluate these realizations by calculating the reservoir flow response. Then, 81 designed simulations were created by factorial design considering the combined realizations of permeability and anisotropy ratio. In a successive step, the most-likely model was considered for the uncertainty quantification of the operational decision parameters through the cyclic GAGD process to restrict the uncertainty space, which leads to obtain the true optimal scenario. The cyclic GAGD operational parameters include durations of injection, soaking, and production and the minimum bottom hole pressure in production wells. The compositional reservoir flow model was again used to evaluate the multiple simulations created by the proxy-based Box–Behnken design and Monte Carlo simulation. The combined geological and production uncertainty workflow gave an idea about the uncertainty or risk space of the predicted reservoir flow response in the future cyclic GAGD process performance.
Highlights
Lacks in geological and reservoir data produces uncertain geomodels and uncertain reservoir flow simulation
The optimal set of these parameters was obtained from the optimal cyclic gas-assisted gravity drainage (GAGD) process, which was addressed in a previous work (Al-Mudhafar et al 2016)
Uncertainty assessment in the varied cyclic parameter levels was conducted through the design of experiments (DoE), proxy modeling, and Monte Carlo simulation
Summary
Lacks in geological and reservoir data produces uncertain geomodels and uncertain reservoir flow simulation. That leads to increase the risk with respect to making trustful decisions in the future reservoir development scenarios (Guyaguler and Horne 2001). It is essential to assess the uncertainty in terms of the geological and production parameters to reduce that risk. Since there is limited geological information required to build efficient reservoir models, there is the inability to precisely evaluate the reservoir performance. That increases the uncertainty associated with forecasting in the future reservoir performance and negatively impacts the economic returns (Zhang 2003). The uncertainty assessment is necessary to construct a solid basis in the reservoir management for development and optimization to improve the decision making (Cruz 2000)
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