Ensemble Based 4D Seismic History Matching: Integration of Different Levels and Types of Seismic Data

Abstract

One of the challenging issues of using 4D seismic data into reservoir history matching is to compare the measured data to the model data in a consistent way. It is important to decide which kind of seismic data can be best used and at which level of history matching process they can be integrated. In this work, we have performed 4D seismic history matching of a sector model based on a North sea reservoir in the ensemble Kalman filter (EnKF) framework and have investigated the effects of different types of time-difference seismic data to update reservoir models. The reservoir-seismic model system consists of a commercial reservoir simulator coupled to an implemented rock physics model and a forward seismic modeling tool based on 1D convolution with weak contrast reflectivity approximation. The objective of this work is to investigate the sensitivity of different combinations of production and seismic data on EnKF model updating. The uncertain static reservoir parameters considered are porosity and permeability; dynamic variables such as pressure and water saturation are conditioned to both production and seismic data. In particular, we are interested to quantify the performance of the wells; also to match seismic data and to estimate the reservoir parameters. In most of the cases of reservoir characterization, time-difference impedance data performed better than time-difference amplitude data and considerably reduced posterior ensemble spread. The matching of seismic data generally improved with the inclusion of time-difference seismic data. In estimating posterior porosity and permeability, seismic difference data provided better estimate than using only production data, especially in aquifer region and also in areas that might be considered for in-fill wells. Thus, in our realistic synthetic case based on a full field reservoir model, we experienced that the integration of seismic data in the elastic domain provided better performance than using amplitude data. In addition, we investigated the effects of vertical resolution of seismic data on EnKF model updating, and showed that the choice of wavelet discretization points can have significant influence on the quality of history matching.

History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations

“…However, other seismic attributes such as amplitudes [23] and time shifts [27,41] have also been used. Fahimuddin et al [16] investigated different kinds of seismic data for history matching with EnKF. They concluded that timedifference impedance data performed better than timedifference amplitude data.…”

Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering

“…To reduce the computational cost in forward simulations, many seismic history matching (SHM) studies use inverted seismic attributes that are obtained through seismic inversions. Such inverted properties can be, for instance, acoustic impedance (see, for example, Emerick and Reynolds, 2012;Emerick et al, 2013;Fahimuddin et al, 2010;Skjervheim et al, 2007) or fluid saturation fronts (see, for example, Abadpour et al, 2013;Leeuwenburgh and Arts, 2014;Trani et al, 2012). One issue in using inverted seismic attributes as the observational data is that, they may not provide uncertainty quantification for the observation errors, since inverted seismic attributes are often obtained using certain deterministic inversion algorithms.…”

“…A number of ensemble-based SHM frameworks have been proposed in the literature. For instance, Abadpour et al (2013);Fahimuddin et al (2010); Katterbauer et al (2015); Leeuwenburgh and Arts (2014); Skjervheim et al (2007);Trani et al (2012) adopt the ensemble Kalman filter (EnKF) or a combination of the EnKF and ensemble Kalman smoother (EnKS), whereas Emerick and Reynolds (2012); Emerick et al (2013); Luo et al (2016) employ the ensemble smoother with multiple data assimilation (ES-MDA), and regularized Levenburg-Marquardt (RLM) based iterative ensemble smoother (RLM-MAC, see Luo et al, 2015), respectively. We note that the history matching algorithm itself is independent of the wavelet-based sparse representation procedure.…”

An Ensemble 4D-Seismic History-Matching Framework With Sparse Representation Based On Wavelet Multiresolution Analysis

“…Seismic history matching using ensemble-based methods has been carried out in some recent works. For instance, Abadpour et al (2013); Fahimuddin et al (2010); Katterbauer et al (2015); Leeuwenburgh and Arts (2014); Skjervheim et al (2007);Trani et al (2012) use the EnKF or a combination of the EnKF and EnKS, whereas Emerick and Reynolds (2012b); Emerick et al (2013) employ the ensemble smoother with multiple data assimilation (ES-MDA). To reduce the computational cost in forward simulations, most of seismic history matching studies adopt inverted seismic properties that are obtained through seismic inversions.…”