Screening Method Using the Derivative-based Global Sensitivity Indices with Application to Reservoir Simulator

Reservoir simulator can involve a large number of uncertain input parameters. Sensitivity analysis can help reservoir engineers focusing on the inputs whose uncertainties have an impact on the model output, which allows reducing the complexity of the model. There are several ways to define the sensitivity indices. A possible quantitative definition is the variance-based sensitivity indices which can quantify the amount of output uncertainty due to the uncertainty of inputs. However, the classical methods to estimate such sensitivity indices in a high-dimensional problem can require a huge number of reservoir model evaluations. Recently, new sensitivity indices based on averaging local derivatives of the model output over the input domain have been introduced. These so-called Derivative-based Global Sensitivity Measures (DGSM) have been proposed to overcome the problem of dimensionality and are linked to total effect indices, which are variance-based sensitivity indices. In this work, we propose a screening method based on revised DGSM indices, which increases the interpretability in some complex cases and has a lower computational cost, as demonstrated by numerical test cases and by an application to a synthetic reservoir test model.