Model calibration for forecasting CO₂-foam EOR field pilot performance in a carbonate reservoir

Application of foam has been found to mitigate challenges associated with field-scale CO₂ floods for Enhanced Oil Recovery (EOR) by providing in-depth mobility control. The field pilots that have been run so far have shown varying results, inferred mainly from interwell tracer studies and production data analysis. A research collaboration has been setup to advance the technology of using foam as mobility control agent for CO₂ EOR, with focus on integrated reservoir modelling to assist technology transfer to high cost environment. A heterogeneous carbonate reservoir onshore in west Texas, USA has been selected for field trial. The reservoir has been waterflooded for more than fifty years, and a significant part of it has been on continuous CO₂ injection for last five years. An inverted five-spot pattern, which had rapid CO₂ breakthrough in adjacent producers and is currently recycling significant amounts of CO₂, has been selected for the study. The pilot is planned for two years with surfactant-alternating-gas injection in the first year, followed by CO₂ injection in the next year.

A reservoir model was created by integrating available static and dynamic information. Since the measurement of static information and production performance is usually imprecise, even the most carefully constructed models do not exactly represent reality. In this paper, we present a workflow that was used to calibrate the reservoir model to historical data for practical forecasting, which takes into account a wide range of uncertainties caused by inaccessibility of information. Laboratory studies were performed with reservoir cores, fluids and selected surfactant to obtain the base values of foam model parameters. As an output, distributions for Key Performance Indicators such as cumulative oil production and CO₂ retention were generated for the proposed pilot to guide further decision making.