History Matching Of Real Production And Seismic Data In The Norne Field

Abstract

Automatic history matching using production and seismic data is still challenging due to the size of seismic datasets. The most severe problem when applying ensemble based methods for assimilating large datasets, is that the uncertainty is usually underestimated due to the limited number of models in the ensemble compared to the dimension of the data, which inevitably leads to an ensemble collapse. Localization and data reduction methods are promising approaches mitigating this problem. In this paper, we present a new robust and flexible workflow for assimilating seismic attributes and production data. The methodology is based on sparse representation of the seismic data, using methods developed for image denoising. The approach can be applied seismic data or inverted seismic attributes obtained from geophysical inverse methods. The seismic response in the forward model is computed using a petroelastic model, that depends on several petrophysical parameters, including lithology, porosity, and saturation. We propose to assimilate production and seismic data sequentially, which makes scaling of different data types redundant, and allows for use of different localization techniques. We use traditional distance-based localization for production data, and a newly developed correlation-based localization technique for seismic data. The latter is necessary because the image denoising method utilize discrete wavelet transforms, which render the seismic data without spatial positions. The workflow is successfully implemented for the Norne field, and an iterative ensemble smoother is used for the sequential assimilation of production data and acoustic impedance. We show that the methodology is robust and ensemble collapse is avoided. Furthermore, the proposed workflow is flexible, as it can be applied to seismic data or inverted seismic properties, and the methodology requires only moderate computer memory. The results show that through this method we can successfully reduce the data mismatch for both production data and seismic data.