DFA Connectivity Advisor: A New Workflow to Use Measured and Modeled Fluid Gradients for Analysis of Reservoir Connectivity
In deepwater and other high-cost environments, reservoir compartmentalization has proven to be a vexing, persistent problem that mandates new approaches for reservoir analysis. In particular, methods involving reservoir fluids can often identify compartments, however, it is far more desirable to identify reservoir connectivity. Downhole fluid analysis (DFA) has enabled cost-effective measurement of compositional gradients of reservoir fluids both vertically and laterally. Modeling of dissolved gas-liquid gradients is readily accomplished using the cubic equation of state (EOS). Modeling of dissolved solid (asphaltenes) - liquid gradients can be achieved using the newly developed Flory-Huggins-Zuo equation of state (FHZ EOS) with its reliance on the nanocolloidal description of asphaltenes within the Yen-Mullins model. The combination of new technology (DFA) and new science (FHZ EOS) provides a powerful means to address reservoir connectivity. It has previously been established that the process of equilibration of reservoir fluids generally requires good reservoir connectivity. Consequently, measured and modeled fluid equilibration is an excellent indicator of reservoir connectivity. However, some reservoir fluid processes are faster than equilibration rates of reservoir fluids. The often slow rate of fluid equilibration makes it a suitable indicator of connectivity. Consequently, measurement of disequilibrium can still be consistent with reservoir connectivity. Moreover, the two fluid gradients, dissolved gas-liquid versus dissolved solid-liquid can be separately responsive to different fluid processes, thereby complicating understanding. A workflow is developed, the DFA Reservoir Connectivity Advisor, to enable interpretation of the implications of measured fluid gradients specifically with regard to reservoir connectivity. Reservoir connectivity is difficult to establish in any event; analyses of fluid gradients are placed in a context of the probability of connectivity, thereby significantly improved risk management.