Nowadays two things are absolutely crucial in petroleum industry: (1) an integrated approach from seismic to reservoir simulation (history matching included) and (2) uncertainty and risk analysis. The first one ensures that the model is coherent with all types of data and the second one drives decision and quantifies risk. Current available hardware and software allow simulating stochastic models with statistical analysis of multiple scenarios. It is important to have a standard workflow that takes into account both aspects and can be applied to all projects. Although geophysicists may not be used to, it is important to include uncertainty analysis on the geophysical model too.
A mature field located in Brazil is used as an example to illustrate the approach suggested above. The field has produced more than 20 million bbl of light oil and now a complementary project development is under study. Despite the amount of oil that has been produced, a significant geological uncertainty still remains. To provide the asset managers with a realistic range of possible outcomes of a project development, a thorough geological stochastic modeling was conducted. Four thousand models were generated considering the following varying parameters: reservoir structure, oil/water contact, porosity, net-to-gross, and initial water saturation. The models were ranked by VOIP and the distribution was sampled (P1, P2, … P100) to go through numerical flow simulation. Permeability uncertainty was introduced by considering two possible scenarios for each model, giving a total of 200 reservoir models. Dynamic data was compared to simulation results through an objective function and those models which gave results too far away from production history were discarded. The VOIP distribution was recalculated.
From this new distribution, the P10, P50, and P90 realizations were picked to be history matched. After that, 27 models were generated through experimental design to consider the variation in the following parameters: (1) geological model, (2) relative permeability, (3) absolute permeability, (4) well productivity index, and (5) the number of wells to be drilled in the project development. The second and third parameters were kept constant in the vicinity of the producing wells so that all 27 models honored production data. A response surface model was generated by interpolation of the 27 flow simulations to obtain 10.000 outcomes for different parameter values. From these results it was possible to perform uncertainty analysis on the prediction.