Streamline-based integration of time-lapse seismic and production data into petroleum reservoir models

Abstract

We have developed an efficient approach of petroleum reservoir model calibration that integrates 4D seismic surveys together with well-production data. The approach is particularly well-suited for the calibration of high-resolution reservoir properties (permeability) because the field-scale seismic data are areally dense, whereas the production data are effectively averaged over interwell spacing. The joint calibration procedure is performed using streamline-based sensitivities derived from finite-difference flow simulation. The inverted seismic data (i.e., changes in elastic impedance or fluid saturations) are distributed as a 3D high-resolution grid cell property. The sensitivities of the seismic and production surveillance data to perturbations in absolute permeability at individual grid cells are efficiently computed via semianalytical streamline techniques. We generalize previous formulations of streamline-based seismic inversion to incorporate realistic field situations such as changing boundary conditions due to infill drilling, pattern conversion, etc. A commercial finite-difference flow simulator is used for reservoir simulation and to generate the time-dependent velocity fields through which streamlines are traced and the sensitivity coefficients are computed. The commercial simulator allows us to incorporate detailed physical processes including compressibility and nonconvective forces, e.g., capillary pressure effects, while the streamline trajectories provide a rapid evaluation of the sensitivities. The efficacy of our proposed approach was tested with synthetic and field applications. The synthetic example was the Society of Petroleum Engineers benchmark Brugge field case. The field example involves waterflooding of a North Sea reservoir with multiple seismic surveys. In both cases, the advantages of incorporating the time-lapse variations were clearly demonstrated through improved estimation of the permeability heterogeneity, fluid saturation evolution, and swept and drained volumes. The value of the seismic data integration was in particular proven through the identification of the continuity in reservoir sands and barriers, and by the preservation of geologic realism in the calibrated model.

Reservoir characterization based upon the onset of time-lapse amplitude changes

“…Estimating reservoir properties such as permeability, based on onset times, contrasts with previous efforts that relied upon timelapse amplitude, traveltime, and waveform changes (Huang et al, 1998;Vasco et al, 2004;Vasco, 2004a;Dong and Oliver, 2005;MacBeth and Al-Maskeri, 2006;Dadashpour et al, 2008Dadashpour et al, , 2009Feng and Mannseth, 2010;Rey et al, 2012). Here, we demonstrate that onset times, extracted from seismic time-lapse reflection data recorded in a few surveys, can provide a reliable basis for reservoir characterization.…”

Using onset times from frequent geophysical surveys to understand reservoir fluid flow and to estimate flow properties

“…For a three-dimensional model we can compute trajectories from each grid block where the saturation has changed to a point on the injection well Geophysical Journal International 21 by streamline simulation (Datta-Gupta and King 2007). We can use time-of-flight methods to relate the arrival or onset time to reservoir properties along the flow paths or streamlines (Vasco et al 1999, Vasco et al 2005, Rey et al 2012, Vasco and Datta-Gupta 2016, Watanabe et al 2017.…”

Integration of time-lapse seismic data using the onset time approach: The impact of seismic survey frequency

“…A number of successful field applications of reservoir management based on 4D seismic data have demonstrated its power and efficacy (Behrens et al, 2002;Fahimuddin et al, 2010;Landrø et al, 2001;Landrø et al, 1999;Watanabe et al, 2017;Hetz et al, 2017). The integration of 4D seismic data into high-resolution geologic model usually involves least-squares-based minimization to match the simulated seismic response and the observed seismic signals (Dadashpour, Mohsen et al, 2010;Dadashpour et al, 2009;Falcone et al, 2004;Gosselin et al, 2003;Gosselin et al, 2001;Rey et al, 2009;Rey et al, 2012;Rwechungura, R.W. et al, 2012;Vasco, 2004;Watanabe et al, 2017;Tang et al, 2018).…”