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Optimization of subsurface CO2 injection based on neural network surrogate modeling

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Abstract

This study presents a workflow to optimize the location of CO2 injectors in order to maximize stored volume and prevent fault reactivation due to increases of pore pressure. We combine coupled reservoir flow and geomechanics simulations with neural network surrogate models and find the best injector location based on net present environmental value (NPEV). The surrogate models can complement the numerical reservoir simulations and efficiently optimize the well location and injection rate via the steepest ascent algorithm. We apply the workflow to the Brugge field, a closed synthetic reservoir that incorporates multiple faults, as an example. The surrogate model predicts a NPEV of approximately 0.19 billion carbon credits for the Brugge field, which is consistent with the corresponding reservoir simulation. NPEV will change under different safety factors that serve as an additional injection control to enhance the safety of injection. This proposed workflow could be readily applied to other similar reservoir models.

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Acknowledgements

This work was supported by ExxonMobil through its membership in The University of Texas at Austin Energy Institute. The authors are thankful to the ExxonMobil team for providing meaningful comments and generating the discussions that helped guide this research.

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  1. Hildebrand Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, 200 E. Dean Keeton Street, Stop C0300, Austin, TX, 78712-1585, USA

    Zhuang Sun, Jianping Xu, D. Nicolas Espinoza & Matthew T. Balhoff

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  1. Zhuang Sun
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Sun, Z., Xu, J., Espinoza, D.N. et al. Optimization of subsurface CO2 injection based on neural network surrogate modeling. Comput Geosci 25, 1887–1898 (2021). https://doi.org/10.1007/s10596-021-10092-9

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