On the value of 3D seismic amplitude data to reduce uncertainty in the forecast of reservoir production

Abstract

Three-dimensional (3D) seismic data are commonly used to identify the size and shape of putative flow barriers in hydrocarbon reservoirs. It is less clear to what extent determining the spatial distribution of engineering properties (e.g., porosity, permeability, pressures, and fluid saturations) can improve predictions (i.e., improve accuracy and reduce uncertainty) of hydrocarbon recovery, given the multiple non-linear and often noisy transformations required to make a prediction. Determining the worth of seismic data in predicting dynamic fluid production is one of the goals of this paper. We have approached the problem of assessing uncertainty in production forecasts by constructing a synthetic reservoir model that exhibits much of the geometrical and petrophysical complexity encountered in clastic hydrocarbon reservoirs. This benchmark model was constructed using space-dependent, statistical relationships between petrophysical variables and seismic parameters. We numerically simulated a waterflood in the model to reproduce time-varying reservoir conditions. Subsequently, a rock physics/fluid substitution model that accounts for compaction and pressure was used to calculate elastic parameters. Pre-stack and post-stack 3D seismic amplitude data (i.e., time-domain amplitude variations of elastic responses) were simulated using local one-dimensional approximations. The seismic data were also contaminated with additive noise to replicate actual data acquisition and processing errors. We then attempted to estimate the original distribution of petrophysical properties and to forecast oil production based on limited and inaccurate spatial knowledge of the reservoir acquired from wells and 3D seismic amplitude data. We compared the multiple realizations of the various predictions against predictions with a reference model. The use of seismic amplitude data to construct static reservoir models affected production performance variables in different ways. For example, seismic amplitude data did not uniformly improve the variability of the predictions of water breakthrough time; other quantities, such as cumulative recovery after the onset of production, did exhibit an uncertainty reduction as did a global measure of recovery. We evaluate how different degrees of spatial correlation strength between seismic and petrophysical parameters may ultimately affect the ensuing uncertainty in production forecasts. Most of the predictions exhibited a bias in that there was a significant deviation between the medians of the realizations and that the value from the reference case. This bias was evidently caused by noise in the various transforms (some of which we introduced deliberately) coupled with nonlinearity. The key nonlinearities seem to be in the numerical simulation itself, specifically in the transformation from porosity to permeability, in the relative permeability relationships, and in the conservation equations themselves.