3D seismic data assimilation to reduce uncertainties in reservoir simulation considering model errors

摘要

Decisions in petroleum reservoir management usually involve high level of uncertainties. Therefore, information of different types is used to calibrate reservoir models for the production forecasts and decision analysis. One source of information is 3D seismic, which is highly correlated to petrophysical properties. These properties are a major source of uncertainty. The incorporation of 3D seismic data in flow models is affected by errors caused by discretization, scale differences, seismic modeling uncertainties, seismic propagation related distortions, among others. Nevertheless, these errors are commonly neglected in conventional model calibration workflows. This work treats seismic resolution loss as a form of model error that needs to be considered in the data assimilation process. In our tests, we used the synthetic data from a realistic benchmark case. First, we extended the methodology proposed by Oliver and Alfonzo (2018a) to 3D seismic data assimilation. We focus on the model improvement by estimating a "total" observation error covariance matrix. Furthermore, we reduced the influence of systematic errors by including a simple analytical function in our forward model. The function is defined based on physical premises and the parameter is calibrated in the data assimilation workflow. This procedure increases the dimension of the problem. The error covariance update improved the reservoir volume characterization in all of our tests. Moreover, we show that the update provides a way to improve the determination of the residual weights in the data assimilation problem. These weights are difficult to define in practice and the results were relatively insensitive to the initial values. By using the proposed methodology, we were able to improve the reservoir volume calibration using a relatively low-resolution data. If the correlated errors were neglected, the data assimilation would lead to implausible parameter distributions.