Model calibration for forecasting CO2-foam enhanced oil recovery 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 inter-well tracer studies and production data analysis. A research collaboration has been set up to advance the technology of using foam as a mobility control agent for CO2 EOR, with focus on integrated reservoir modelling to assist technology transfer to a high-cost environment. A heterogeneous carbonate reservoir onshore in west Texas, USA has been selected for the field trial. The reservoir has been waterflooded for more than 50 years, and a significant part of it has been on continuous CO2 injection for the last 5 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 2 years with surfactant alternating gas (SAG) 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 the 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.