Proposal Abstract
The Dynamic Numerical Simulation in Flowlines (DNSFL) is an alternative tool adapted for handling Fine Scale Dynamic Models. DNSLF uncouples reservoir geometry and heterogeneity from transport equations, solving problems dominated by convective flows in a faster and computationally more efficient way. This allows to build Models of greater spatial discretization and, consequently, to better represent reservoir heterogeneity. This feature has been particularly relevant for studying the current case, that consists of great extension reservoirs with considerable facial and stratigraphical variations.
In this paper a workflow is presented, using the above DNSLF properties, in a Global History Matching to develop the "Fine" Scale Geologic Model Calibration and the Definition of a Preliminary Static-Dynamic Model of a giant field of Neuquén Basin, Argentina.
The analyzed field is constituted by fluvio-lacustrine deposits, nine sand-clay cycles (only six of them were modeled), partially connected reservoirs; with 380 actives wells in commingled production and water injection; and with a long and detailed history of simultaneous primary and secondary events.
CPU runtimes around 70 minutes were achieved using a 808860 grid cells Model, with 111 quarterly based timesteps in a Pentium 4 PC, 2.2GHz CPU and 2GB RAM. This showed that it was possible to work with a big Model in short processing time.
With this preliminary model, based on a well-groups-level global history matching, a clear conceptualization of the current primary and secondary production mechanisms, and its productive behavior, was obtained. This reservoir conceptualization was a strong guide for the Global History Matching process.
Geostatistical and Upscaling procedures applied for the definition of the Simulation Model could be evaluated by means of sensitivities analysis on some model parameters.
This preliminary model allowed sensitivities to make recommendations on future work stages, including Geological Model refinements and detailed History Matching.
