Uncertainty, sensitivity analysis and optimization of a reservoir geological model

Abstract

Geological models are mainly obtained via interpolation of simulations using geostatistical algorithms, and the simulation results are highly uncertain. In this paper, several factors that have great influences on model results are analyzed. Through an analysis of the sensitive uncertain factors, it is found that the uncertainty of the lithofacies model has the greatest influence on the geological model, and the variogram has the second greatest influence. Therefore, ensuring the rationality of lithologic model can effectively reduce the uncertainty of the model. It is found that using seismic impedance as constraints can effectively improve the accuracy of the lithofacies model, and one can use the seismic impedance to obtain a reasonable variogram (effectively overcoming the shortage of well data), thus reducing the uncertainty of the model. Although the stochastic simulation technology used in geological modeling can yield many simulation results, only a limited number of models can be applied in practical work. In this paper, a probabilistic analysis method is proposed to optimize the model, which can provide a reference for business decision-making and risk assessment of oil and gas companies. Reserve uncertainty analysis in the exploration stage can determine the most probable reserve of the reservoir through the probabilistic characteristics of the reserve so as to determine the next exploration target. In the development stage, probabilistic analyses of the porosity, remaining oil saturation and other parameters related to oil field production can maximize the development of remaining oil. The idea of model optimization based on sensitivity analysis of model uncertainties proposed in this paper can effectively reduce the uncertainty of the model, improve the accuracy of the geological model and meet the needs of different research stages and levels.