History matching is used to constrain flow simulations and reduce uncertainty in forecasts. In this work, we revisited some fundamental engineering tools for predicting waterflooding behavior to better understand the flaws in our simulation and thus find some models which are more accurate with better matching. The Craig-Geffen-Morse (CGM) analytical method was used to predict recovery performance calculations and it was simple enough which can be applied in a spreadsheet. In this study, the analytical approach of history matching was applied to a layered reservoir from a shallow marine deposit which was composed of different facies includes lower shoreface facies (LSF), middle shoreface facies (MSF) and upper shoreface facies (USF). Truncated Gaussian Simulation (TGS) is often used to stochastically distribute the facies in the geological model around a deterministic mean representation. The actual distribution is often hard to determine. Starting with the deterministic element of the facies distributions, corrections were made by matching the CGM method predictions to historical data. These corrections were amalgamated in the model and produced a much better history match. Further, the modifications were used to condition the stochastic simulator to provide a geologically more robust model that also matched history. The results showed that the variation of the total field production rate (FPR) between the deterministic model and history data was reduced by about 19.8% (from 21.52% to 1.73%) after applying history match analytically.
- Craig-Geffen-Morse analytical method
- Uncertainty reduction
- Waterflood performance
- Improving geological models
- History matching