Integrated reservoir modeling at the Illinois Basin – Decatur Project

Sallie Greenberg, Ozgur Senel, Robert Will and Robert J. Butsch

Greenhouse Gases: Science and Technology, 2014, vol. 4, issue 5, 662-684

Abstract: Injecting carbon dioxide (CO 2 ) into the subsurface comes with the added responsibility of understanding the movement of the CO 2 once injected, and ensuring that it remains contained in the target reservoir. Consequently, a significant part of the sequestration process is to build and maintain models of the subsurface that will predict the flow of the injected CO 2 . These models need to integrate all data available so that the subsurface movement of the CO 2 can be predicted should changes to the injection parameters be made. As the CO 2 plume is monitored by various measurements, the model needs to be flexible enough to be updated to account for any difference between predicted and known behavior. An integrated predictive reservoir model was created for the Illinois Basin – Decatur Project utilizing a variety of data collected from 2007 until January 2013. The evolution of this model will be discussed. Prior to the beginning of injection, in November 2011, reservoir models and numerical simulations were used extensively to design the wellbore, completions, and well tests; optimize predicted injectivity; simulate the CO 2 subsurface migration development and pressure perturbation; analyze and understand well test results; and quantify uncertainties in predictions. Now, with the help of dynamic injection data collected, the calibration of the model has been accomplished by honoring the observations and this facilitates making more informed forecasts. Our analysis indicates a laterally developing CO 2 subsurface migration, thus reducing the near to mid‐term expectation of CO 2 reaching the seal and upper sections of target reservoir. Further planned modeling efforts include additional model refinements required for anticipated future applications including regulatory compliance and multi‐physics data integration.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:wly:greenh:v:4:y:2014:i:5:p:662-684

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