Abstract
Reservoir simulation models are used to forecast future reservoir behavior and to optimally manage reservoir production. These models require specification of hundreds of thousands of parameters, some of which may be determined from measurements along well paths, but the distance between wells can be large and the formations in which oil and gas are found are almost always heterogeneous with many geological complexities so many of the reservoir parameters are poorly constrained by well data. Additional constraints on the values of the parameters are provided by general geologic knowledge, and other constraints are provided by historical measurements of production and injection behavior. This type of information is often not sufficient to identify locations of either currently remaining oil, or to provide accurate forecasts where oil will remain at the end of project life. The repeated use of surface seismic surveys offers the promise of providing observations of locations of changes in physical properties between wells, thus reducing uncertainty in predictions of future reservoir behavior. Unfortunately, while methodologies for assimilation of 4D seismic data have demonstrated substantial value in synthetic model studies, the application to real fields has not been as successful. In this paper, we review the literature on 4D seismic history matching (SHM), focusing discussions on the aspects of the problem that make it more difficult than the more traditional production history matching. In particular, we discuss the possible choices for seismic attributes that can be used for comparison between observed or modeled attribute to determine the properties of the reservoir and the difficulty of estimating the magnitude of the noise or bias in the data. Depending on the level of matching, the bias may result from errors in the forward modeling, or errors in the inversion. Much of the practical literature has focused on methodologies for reducing the effect of bias or modeling error either through choice of attribute, or by appropriate weighting of data. Applications to field cases appear to have been at least partially successful, although quantitative assessment of the history matches and the improvements in forecast is difficult.
Highlights
Reservoir simulation models are increasingly used for making forecasts of future reservoir behavior
The simulation of the 4D baseline seismic data from the static reservoir model should be consistent with the structural 3D seismic interpretation
The observed 4D attributes used in the HM workflow should preferably be matched to synthetic versions of the same attributes computed from the output from a full sim2seis workflow (Sagitov and Stephen, 2012)
Summary
Reservoir simulation models are increasingly used for making forecasts of future reservoir behavior. History matching of 4D seismic data is difficult even on synthetic ‘‘twin’’ experiments, the application to actual field data has additional challenges, so it has generally been necessary to make a number of simplifying assumptions when history matching real 4D seismic data These assumptions include (1) the use of relatively small numbers of history matched models to provide estimates of uncertainty in forecasts, (2) qualitative interpretation of data such as interpreted change in oil–water-contact from seismic for assimilation, (3) Gaussian approximations of uncertainty and neglect of scenario uncertainty, (4) neglect of differences in scale between observed seismic data and simulated seismic data, (5) neglect of imperfections in the simulators and neglect of errors in quantification of initial uncertainty in parameters, and (6) neglect of correlations in data error. Seismic attributes such as inverted acoustic impedance are used to constrain the distribution of various reservoir properties (i.e., porosity, net-to-gross ratio, permeability, etc.) geostatistically over the reservoir zones (Bogan et al, 2003). 4D or time-lapse seismic data are sensitive to changes in pressure and saturation within the reservoir, potentially helping in identification of fluid flow barriers and reservoir flow properties
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