Data-driven Discrete Well Affinity (DiWA) Model for Production Forecast

Abstract

Evaluating an adequate computer model representing the characteristics of a subsurface reservoir is of great importance in the oil and gas industry. With recent data acquisition developments, high-resolution geological models can be generated for the subsurface reservoirs; however, uncertainties of geological data and too many details in the model have brought up remarkable limitations for application of high-fidelity geo-models in reservoir management. To generate efficient low-resolution models, which are relatively representative of the reservoir dynamic behaviour, data-driven models are proposed. The data-driven models are trained principally based on the production history of the reservoir with primary knowledge of structural information. The Data-driven Discrete Well Affinity (DiWA) model is designed to generate efficient proxy models that require basic geological information and full production history of the reservoir. Since it benefits the computational power of the Delft Advanced Terra Simulator (DARTS) and adjoint gradient-based optimization, the DiWA model has become an efficient framework in terms of accuracy and computational costs. This research shows that the DiWA model can relatively reconstruct the reference reservoir parameters out of an ensemble of priors, and the final results can be improved utilizing closer prior knowledge to the true response. Additionally, it is demonstrated that the performance of the DiWA model in a complex, realistic oil field can be enhanced using a comprehensive objective function, applying appropriate well controls, understanding the underlying driving mechanisms, using sufficient degrees of freedom, and applying consistent boundary conditions.