Abstract
Three years ago (DDDAS at the 2017 ASME Meeting) we looked at the speaker’s top ten list of how DDDAS can save the world. Now as we adjust to life under the COVID-19 pandemic and a 2020 Conference in the virtual format, our world literally seeks rescue/saving. Under these circumstances, we revisit the top ten list and first consider briefly the dynamic data-driven aspects of the COVID-19 challenges from the speaker’s experiences in the biotech/pharma industry and then move on to the more optimistic challenge of securing our energy future and life after the pandemic.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mason, C.F., Muehlenbachs, L.A., Olmstead, S.M.: The economics of shale gas development. Ann. Rev. Resour. Econ. 7(1), 269–289 (2015)
Blasch, E., Ravela, S., Aved, A.: Handbook of Dynamic Data Driven Applications Systems. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95504-9
Darema, F.: New software architecture for complex applications development and runtime support. Int. J. High-Perform. Comput. (Special Issue on Programming Environments, Clusters, and Computational Grids for Scientific Computing) 14 (2000)
Smith, P.J.: Clean and secure energy from domestic oil shale and oil sands resources-quarterly progress report July 2011 to September 2011
Parashar, M., et al.: Application of grid-enabled technologies for solving optimization problems in data-driven reservoir studies. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 805–812. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24688-6_104
Oden, J.T., et al.: Revolutionizing engineering science through simulation: A report of the national science foundation blue ribbon panel on simulation-based engineering science. National Science Foundation, Arlington, VA (2006)
Douglas, C.C.: An open framework for dynamic big-data-driven application systems (DDDAS) development. Proc. Comput. Sci. 29, 1246–1255 (2014)
Parashar, M., et al.: Towards dynamic data-driven optimization of oil well placement. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds.) ICCS 2005. LNCS, vol. 3515, pp. 656–663. Springer, Heidelberg (2005). https://doi.org/10.1007/11428848_85
Yan, J., Wang, L., Chen, L., Zhao, L., Huang, B.: A dynamic remote sensing data-driven approach for oil spill simulation in the sea. Remote Sens. 7(6), 7105–7125 (2015)
Chen, X., Zhang, D., Wang, Y., Wang, L., Zomaya, A., Hu, S.: Offshore oil spill monitoring and detection: improving risk management for offshore petroleum cyber-physical systems. In: 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 841–846. IEEE (2017)
Pecher, P.K.: A DDDAS framework for managing online transportation systems. Ph.D. thesis, Georgia Institute of Technology (2018)
Wang, S., Ellett, K.M., Ardekani, A.M.: Assessing the utility of high-level CO2 storage and utilization resource estimates for CCS system modelling. Energy Proc. 114, 4658–4665 (2017)
Bello, O., et al.: Application of artificial intelligence techniques in drilling system design and operations: a state of the art review and future research pathways. In: SPE Nigeria Annual International Conference and Exhibition. Society of Petroleum Engineers (2016)
Sandrea, I., Sandrea, R.: Recovery factors leave vast target for EOR technologies. Oil Gas J. 105(41), 44–48 (2007)
Muggeridge, A., et al.: Recovery rates, enhanced oil recovery and technological limits. Philos. Trans. Roy. Soc. A: Math. Phy. Eng. Sci. 372(2006), 20120320 (2014)
Shukla, A., Karki, H.: Application of robotics in offshore oil and gas industry a review part II. Robot. Auton. Syst. 75, 508–524 (2016)
Agwu, O.E., Akpabio, J.U., Alabi, S.B., Dosunmu, A.: Artificial intelligence techniques and their applications in drilling fluid engineering: a review. J. Pet. Sci. Eng. 167, 300–315 (2018)
Khan, W.Z., Aalsalem, M.Y., Khan, M.K., Hossain, Md.S., Atiquzzaman, M.: A reliable internet of things based architecture for oil and gas industry. In: 2017 19th International Conference on Advanced Communication Technology (ICACT), pp. 705–710. IEEE (2017)
Douglas, C.C., et al.: Advantages of multiscale detection of defective pills during manufacturing. In: Zhang, W., Chen, Z., Douglas, C.C., Tong, W. (eds.) HPCA 2009. LNCS, vol. 5938, pp. 8–16. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11842-5_2
Li, C.-S., Darema, F., Chang, V.: Distributed behavior model orchestration in cognitive internet of things solution. Enterp. Inf. Syst. 12(4), 414–434 (2018)
Hu, X.: Dynamic data-driven simulation: connecting real-time data with simulation. In: Yilmaz, L. (ed.) Concepts and Methodologies for Modeling and Simulation. SFMA, pp. 67–84. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15096-3_4
Priyanka, E.B., Maheswari, C., Thangavel, S.: A smart-integrated IoT module for intelligent transportation in oil industry. Int. J. Numer. Model.: Electron. Netw. Devices Fields
Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: a platform for internet of things and analytics. In: Bessis, N., Dobre, C. (eds.) Big Data and Internet of Things: A Roadmap for Smart Environments. SCI, vol. 546, pp. 169–186. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05029-4_7
Goldberg, D.E., Kuo, C.H.: Genetic algorithms in pipeline optimization. J. Comput. Civ. Eng. 1(2), 128–141 (1987)
Nygreen, B., Haugen, K.: Applied mathematical programming in norwegian petroleum field and pipeline development: some highlights from the last 30 years. In: Bjørndal, E., Bjørndal, M., Pardalos, P., Rönnqvist, M. (eds.) Energy, Natural Resources and Environmental Economics. ENERGY, pp. 59–69 (2010). Springer, Heidelberg. https://doi.org/10.1007/978-3-642-12067-1_4
Bohannon, J.M., et al.: A linear programming model for optimum development of multi-reservoir pipeline systems. J. Pet. Technol. 22(11), 1–429 (1970)
Neiro, S.M., Pinto, J.M.: A general modeling framework for the operational planning of petroleum supply chains. Comput. Chem. Eng. 28(6–7), 871–896 (2004)
Patel, H., Prajapati, D., Mahida, D., Shah, M.: Transforming petroleum downstream sector through big data: a holistic review. J. Pet. Explor. Prod. Technol. 10, 2601–2611 (2020). https://doi.org/10.1007/s13202-020-00889-2
Ramapriya, G.M., Tawarmalani, M., Agrawal, R.: Thermal coupling links to liquid-only transfer streams: an enumeration method for new FTC dividing wall columns. AIChE J. 62(4), 1200–1211 (2016)
Di Iorio, J.R., et al.: The dynamic nature of brønsted acid sites in cu–zeolites during no x selective catalytic reduction: quantification by gas-phase ammonia titration. Top. Catal. 58(7–9), 424–434 (2015). https://doi.org/10.1007/s11244-015-0387-8
Childers, D.J., Schweitzer, N.M., Shahari, S.M.K., Rioux, R.M., Miller, J.T., Meyer, R.J.: Modifying structure-sensitive reactions by addition of Zn to Pd. J. Catal. 318, 75–84 (2014)
Acknowledgement
We thank the CISTAR NSF-ERC team and Director Fabio Ribeiro for insightful discussions on the downstream shale revolution. We also thank Nathan Schultheiss for extensive discussions on the current state of the upstream and midstream O&G; this extended abstract is a summary version of a longer DDDAS O&G chapter.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, S., Kim, S. (2020). Revisiting the Top Ten Ways that DDDAS Can Save the World with an Update in the BioInfoSciences Area and on the Energy Bridge. In: Darema, F., Blasch, E., Ravela, S., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2020. Lecture Notes in Computer Science(), vol 12312. Springer, Cham. https://doi.org/10.1007/978-3-030-61725-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-61725-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61724-0
Online ISBN: 978-3-030-61725-7
eBook Packages: Computer ScienceComputer Science (R0)