Analytical well-test solutions are mainly derived for simplified and idealized reservoir models and therefore cannot always honour the true complexity of real reservoir heterogeneities. Pressure transients in the reservoir average out heterogeneities, and therefore some interpretations may not be relevant and could be misleading. Geological well testing refers to the numerical simulation of transient tests by setting up detailed geological models, within which different scales of heterogeneity are present. The concept of geological well testing described in this paper assists in selecting from multiple equi-probable static models. This approach is used to understand which heterogeneities can influence the pressure transients. In this paper, a low-energy multi-facies fluvial reservoir is studied, for which data from a well test of exceptionally long duration are available. The pervasive low reservoir quality facies and restricted macro cross-flow between the reservoir layers give rise to an effective commingled system of flow into the wellbore (i.e. zero or very low vertical cross-flow between the reservoir units). In our model, facies transitions produce lateral cross-flow transients that result in a ‘double-ramp-effect’ signature in the test response. A sophisticated multi-point statistical (MPS) facies modelling approach is utilized to simulate complex geological heterogeneities and to represent facies spatial connectivity within a set of generated static models. The geological well-test model responses to a real well-testing cycle are then evaluated using dynamic simulation. The pressure match between simulated and recorded data is improved by generating multiple facies and petrophysical realizations, and by applying an engineering-based hybridization algorithm to combine different models that match particular portions of the real well-test response. In this example, the reservoir dynamics are controlled by subtle interaction between high-permeability channels and low-permeability floodplain deposits. Effective integration of geology and dynamic data using modern methods can lead to better reservoir characterization and modelling of such complex reservoir systems.
- © 2014 EAGE/Geological Society of London