A New Approach for Reservoir Characterization

To reliably predict the reservoir performance, an accurate model of the reservoir is necessary. For reservoir simulation purposes, the flow unit model is most practical approach. The flow units are defined according to geological and petrophysical properties that influence the flow of fluids in the reservoir. Identification and prediction of flow units are strongly dependent on the availability of permeability distribution. This need for permeability distribution significantly limits the identification of flow units in reservoirs where permeability measurements are not abundant such as most reservoirs in the Appalachian Basin.In this study, statistical and artificial intelligence techniques were employed to identify flow units based on limited data obtained from core analysis supplemented by mini-permeameter measurements, geological interpretations, and well log data in a heterogeneous oil reservoir in the Appalachian Basin. An innovative methodology was then developed to predict flow units using only well log data. The distribution of flow units in the reservoir was then predicted based on abundant well log data. Finally, permeability and porosity distributions were predicted based on the distribution of the flow units in the reservoir. This approach led to development of a reliable reservoir model. The accuracy of model was verified by successful simulation of the production performance. The methodology presented in this paper can serve as a new guideline for the characterization of heterogeneous reservoirs.