The Synergy between Static Reservoir Modelling and Flow Simulations for the Optimization of a Field Development Plan of a Carbonate Reservoir, Onshore UAE

Abstract

Abstract This paper presents a case study where a full interaction between static reservoir modelling and flow simulation was performed to optimize the development plan of a carbonate Field in Abu Dhabi. Since no reliable seismic attributes could be extracted from the 3D seismic, coupled with the fact the reservoir had only a few wells, several geological scenarios were modeled using both deterministic and stochastic techniques. Sensitivities were performed on variogram ranges, deterministic trends and algorithms. The population of the model with petrophysical properties was carried out using reconciled log and core data in the crestal area of the Field and propagated downflank with the assistance of trends. The simulation of the porosity was done with Sequential Gaussian Simulation and for the permeability a co-simulation with porosity technique was applied. The results of 1D permeability simulation were also tried. Both approaches for permeability modelling were matched with the well test results. Due to the absence of SCAL data and the fact that the open-hole log saturations are not reliable (low resistivity pay in some intervals), an analog Sw curve had to be used. Two extreme Sw curves (min and max) were considered to cover the uncertainty. Different reservoir descriptions (geological/petrophysical scenarios) were subsequently flow simulated and the results evaluated against different development options. The selected models matched water cuts and reservoir pressures without the need for permeability multipliers. Finally, the results on new drilled wells (blind tests), namely log properties, PBU and injectivity test results, showed a good agreement with the models. The selected models were used to compare different patterns of development under multiple pressure support schemes (peripherical and updip water injection, gas vs. water injection, etc.). This paper calls for the importance of building different static models using different geostatistical parameters to address the static-dynamic uncertainties, instead of performing a large number of stochastic realizations.