Development of robust surrogate model for economic performance prediction of oil reservoir production under waterflooding process

Geological uncertainty imposes significant challenge in modeling oil reservoirs. In this paper, a new robust modeling methodology is presented to identify a set of robust surrogate models with unstructured uncertainty for economic performance prediction of an uncertain oil reservoir under waterflooding process. The oil reservoir, being treated as a multi-input, multi-output (MIMO) system in terms of injection inputs and production outputs, is identified within a robust surrogate modeling framework using a frequency-based system identification approach. For this purpose, the concept of unstructured uncertainty model is incorporated with the surrogate modeling in the context of a function block having a norm-bounded uncertainty profile. The identified MIMO surrogate model is integrated with a desired nonlinear net present value (NPV) objective function to synthesize a new modified robust surrogate model in a multi-input, single-output (MISO) configuration form. This new modeling strategy enables direct calculation of economic performance prediction for the target oil reservoir. Finally, applicability of the proposed method is evaluated on "Egg Model" as a three-dimensional synthetic oil reservoir with 8 water injection wells and 4 oil production wells. The results clearly demonstrate that economic performance prediction of the oil reservoir, having uncertain permeability field, lies in the bounds corresponding to the worst-case scenarios of the uncertainty model set.