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Dynamic Data-Driven Application Systems for Reservoir Simulation-Based Optimization: Lessons Learned and Future Trends

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Abstract

Since its introduction in the early 2000s, the Dynamic Data-Driven Applications Systems (DDDAS) paradigm has served as a powerful concept for continuously improving the quality of both models and data embedded in complex dynamical systems. The DDDAS unifying concept enables capabilities to integrate multiple sources and scales of data, mathematical and statistical algorithms, advanced software infrastructures, and diverse applications into a dynamic feedback loop. DDDAS has not only motivated notable scientific and engineering advances on multiple fronts, but it has been also invigorated by the latest technological achievements in artificial intelligence, cloud computing, augmented reality, robotics, edge computing, Internet of Things (IoT), and Big Data. Capabilities to handle more data in a much faster and smarter fashion is paving the road for expanding automation capabilities. The purpose of this chapter is to review the fundamental components that have shaped reservoir-simulation-based optimization in the context of DDDAS. The foundations of each component will be systematically reviewed, followed by a discussion on current and future trends oriented to highlight the outstanding challenges and opportunities of reservoir management problems under the DDDAS paradigm. Moreover, this chapter should be viewed as providing pathways for establishing a synergy between renewable energy and oil and gas industry with the advent of the DDDAS method.

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References

  1. Biomedical Informatics Research Network (BIRN). http://www.nbirn.net/

  2. Dynamic Data Driven Applications Systems (DDDAS). http://www.1dddas.org

  3. Earth Systems Grid (ESG). http://www.earthsystemgrid.org

  4. Grid Physics Network (GriPhyN). http://www.griphyn.org

  5. IPARS: Integrated Parallel Reservoir Simulator. Center for Subsurface Modeling, University of Texas at Austin. https://csm.oden.utexas.edu/

  6. MEDIGRID. http://creatis-www.insa-lyon.fr/MEDIGRID/home.html

  7. Multilevel, Dynamic Data-Driven Application Simulation. http://www.mgnet.org/douglas/ml-dddas.html

  8. The Discover Computational Collaboratory. http://www.discoverportal.org

  9. AbdelBaky, M., Zou, M., Zamani, A.R., Renart, E., Diaz-Montes, J., Parashar, M.: Computing in the continuum: Combining pervasive devices and services to support data-driven applications. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), pp. 1815–1824 (2017)

    Google Scholar 

  10. Agarwal, S., Rajan, K.: Analyzing the performance of NoSQL vs. SQL databases for spatial and aggregate queries. In: Free and Open Source Software for Geospatial (FOSS4G) Conference Proceedings, vol. 17, p. 4 (2017)

    Google Scholar 

  11. Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., Saltz, J.: Hadoopgis: A high performance spatial data warehousing system over mapreduce. In: Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, vol. 6. NIH Public Access (2013)

    Google Scholar 

  12. Al-Hinai, O., Dong, R., Srinivasan, S., Wheeler, M.F.: A new equidimensional fracture model using polyhedral cells for microseismic data sets. Journal of Petroleum Science and Engineering 154, 49–59 (2017)

    Article  Google Scholar 

  13. Albertoni, A., Lake, L.W.: Inferring interwell connectivity only from well-rate fluctuations in waterfloods. SPE Reservoir Evaluation & Engineering 6(01), 6–16 (2003)

    Article  Google Scholar 

  14. Arbogast, T., Cowsar, L.C., Wheeler, M.F., Yotov, I.: Mixed finite element methods on nonmatching multiblock grids. SIAM Journal on Numerical Analysis 37(4), 1295–1315 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  15. Armstrong, R., Kumfert, G., McInnes, L.C., Parker, S., Allan, B., Sottile, M., Epperly, T., Dahlgren, T.: The CCA component model for high-performance scientific computing. Concurrency and Computation: Practice and Experience 18(2), 215–229 (2006)

    Article  Google Scholar 

  16. Baig, F., Vo, H., Kurc, T., Saltz, J., Wang, F.: Sparkgis: Resource aware efficient in-memory spatial query processing. In: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–10 (2017)

    Google Scholar 

  17. Balouek-Thomert, D., Renart, E.G., Zamani, A.R., Simonet, A., Parashar, M.: Towards a computing continuum: Enabling edge-to-cloud integration for data driven workflows. The International Journal of High Performance Computing Applications 33(6), 1159–1174 (2019). DOI https://doi.org/10.1177/1094342019877383

    Article  Google Scholar 

  18. Balouek-Thomert, D., Rodero, I., Parashar, M.: Harnessing the computing continuum for urgent science. International Workshop on Distributed Cloud Computing (2020). http://par.nsf.gov/biblio/10187425

  19. Bangerth, W., Klie, H., Matossian, V., Parashar, M., Wheeler, M.F.: An autonomic reservoir framework for the stochastic optimization of well placement. Cluster Computing 8(4), 255–269 (2005)

    Article  Google Scholar 

  20. Bangerth, W., Klie, H., Wheeler, M., Stoffa, P., Sen, M.: On optimization algorithms for the reservoir oil well placement problem. Computational Geosciences 10(3), 303–319 (2006)

    Article  MATH  Google Scholar 

  21. Bernstein, D.: Containers and cloud: From lxc to docker to kubernetes. IEEE Cloud Computing 1(3), 81–84 (2014)

    Article  Google Scholar 

  22. Beynon, M., Chang, C., Catalyurek, U., Kurc, T., Sussman, A., Andrade, H., Ferreira, R., Saltz, J.: Processing large-scale multi-dimensional data in parallel and distributed environments. Parallel Computing 28(5), 827–859 (2002)

    Article  Google Scholar 

  23. Beynon, M., Ferreira, R., Kurc, T., Sussman, A., Saltz, J.: Datacutter: Middleware for filtering very large scientific datasets on archival storage systems. In: IEEE symposium on mass storage systems, pp. 119–134 (2000)

    Google Scholar 

  24. Beynon, M.D., Kurc, T., Catalyurek, U., Chang, C., Sussman, A., Saltz, J.: Distributed processing of very large datasets with datacutter. Parallel Computing 27(11), 1457–1478 (2001)

    Article  MATH  Google Scholar 

  25. Beynon, M.D., Sussman, A., Catalyurek, U., Kurc, T., Saltz, J.: Performance optimization for data intensive grid applications. In: Proceedings Third Annual International Workshop on Active Middleware Services, pp. 97–105. IEEE (2001)

    Google Scholar 

  26. Blasch, E., Xu, R., Nikouei, S.Y., Chen, Y.: A study of lightweight dddas architecture for real-time public safety applications through hybrid simulation. In: 2019 Winter Simulation Conference (WSC), pp. 762–773. IEEE (2019)

    Chapter  Google Scholar 

  27. Brahim, M.B., Drira, W., Filali, F., Hamdi, N.: Spatial data extension for Cassandra NoSQL database. Journal of Big Data 3(1), 11 (2016)

    Article  Google Scholar 

  28. Brunton, S.L., Kutz, N.J.: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems and Control. Cambridge University Press (2019)

    Book  MATH  Google Scholar 

  29. Cardoso, M.A., Durlofsky, L.J.: Linearized reduced-order models for subsurface flow simulation. Journal of Computational Physics 229(3), 681–700 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  30. Chen, H., Klie, H., Wang, Q.: A black-box interpolation method to accelerate reservoir simulation solutions. In: SPE Reservoir Simulation Symposium, 163614-MS. Society of Petroleum Engineers, The Woodlands, TX (2013)

    Google Scholar 

  31. Darema, F.: Grid computing and beyond: The context of dynamic data driven applications systems. Proceedings of the IEEE 93(3), 692–697 (2005). https://doi.org/10.1109/JPROC.2004.842783

    Article  Google Scholar 

  32. Davis, P., Subedi, P., Duan, S., Ricketson, L., Hittinger, J.A., Parashar, M.: Benesh: a programming model for coupled scientific workflows. In: 2020 IEEE/ACM 5th International Workshop on Extreme Scale Programming Models and Middleware (ESPM2). IEEE (2020)

    Google Scholar 

  33. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  34. Diaz-Montes, J., AbdelBaky, M., Zou, M., Parashar, M.: Cometcloud: Enabling software-defined federations for end-to-end application workflows. IEEE Internet Computing 19(1), 69–73 (2015)

    Article  Google Scholar 

  35. Docan, C., Parashar, M., Klasky, S.: Dataspaces: an interaction and coordination framework for coupled simulation workflows. Cluster Computing 15(2), 163–181 (2012)

    Article  Google Scholar 

  36. Dong, R., Lee, S., Wheeler, M.: Numerical simulation of matrix acidizing in fractured carbonate reservoirs using adaptive enriched Galerkin method. In: SPE Reservoir Simulation Conference. Society of Petroleum Engineers (2019)

    Google Scholar 

  37. Dong, R., Wang, Q., Wheeler, M.F.: Prediction of mechanical stability of acidizing-induced wormholes through coupled hydro-chemo-mechanical simulation. In: 53rd US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association (2019)

    Google Scholar 

  38. Dong, R., Wheeler, M.F., Ma, K., Su, H.: A 3D acid transport model for acid fracturing treatments with viscous fingering. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers (2020)

    Google Scholar 

  39. Douglas, C.C., Shannon, C.E., Efendiev, Y., Ewing, R., Ginting, V., Lazarov, R., Cole, M.J., Jones, G., Johnson, C.R., Simpson, J.: A note on data-driven contaminant simulation. In: International Conference on Computational Science, pp. 701–708. Springer (2004)

    Google Scholar 

  40. Duan, S., Parashar, M.: Scalable crash consistency for staging-based in-situ scientific workflows. In: 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 340–348. IEEE (2020)

    Chapter  Google Scholar 

  41. Duan, S., Subedi, P., Davis, P., Teranishi, K., Kolla, H., Gamell, M., Parashar, M.: Corec: Scalable and resilient in-memory data staging for in-situ workflows. ACM Transactions on Parallel Computing (TOPC) 7(2), 1–29 (2020)

    Article  Google Scholar 

  42. Duan, S., Subedi, P., Davis, P.E., Parashar, M.: Addressing data resiliency for staging based scientific workflows. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, p. 87. ACM (2019)

    Google Scholar 

  43. Duan, S., Subedi, P., Teranishi, K., Davis, P., Kolla, H., Gamell, M., Parashar, M.: Scalable data resilience for in-memory data staging. In: 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 105–115. IEEE (2018)

    Chapter  Google Scholar 

  44. Efendiev, Y., Galvis, J., Wu, X.H.: Multiscale finite element methods for high-contrast problems using local spectral basis functions. Journal of Computational Physics 230(4), 937–955 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  45. Eldawy, A., Mokbel, M.F.:Spatialhadoop:A mapreduce framework for spatial data. In: 2015 IEEE 31st international conference on Data Engineering, pp. 1352–1363. IEEE (2015)

    Google Scholar 

  46. Emerick, A.A., Reynolds, A.C.: Investigation of the sampling performance of ensemble-based methods with a simple reservoir model. Computational Geosciences 17(2), 325–350 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  47. Erik, B., Ashdown, J., Varela, C., Kopsaftopoulos, F., Newkirk, R.: Dynamic data driven analytics for multi-domain environments. In: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, vol. 11006, p. 1100604. International Society for Optics and Photonics (2019)

    Google Scholar 

  48. Fauvel, K., Daniel Balouek-Thomert, D., Melgar, D., Silva, P., Simonet, A., Antoniu, G., Costan, A., Masson, V., Parashar, M., Rodero, I., Termier, A.: A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning. In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (2020)

    Google Scholar 

  49. Florez, H., Gildin, E.: Model-order reduction of coupled flow and geomechanics in ultra-low permeability ULP reservoirs. In: SPE Reservoir Simulation Conference. Society of Petroleum Engineers (2019)

    Google Scholar 

  50. Foster, I.: Globus toolkit version 4: Software for service-oriented systems. Journal of computer science and technology 21(4), 513–520 (2006)

    Article  Google Scholar 

  51. Fujimoto, R., Joseph, B., Blasch, E., Cai, W., Jin, D., Lee, S., Son, Y.J.: Dynamic data driven application systems: Research challenges and opportunities. In: Proceedings of the 2018 Winter Simulation Conference, pp. 664–678. IEEE (2018)

    Google Scholar 

  52. Ganis, B., Pencheva, G., Wheeler, M.F.: Adaptive mesh refinement with an enhancedvelocitymixedfiniteelementmethodonsemi-structuredgridsusing a fully coupled solver. Computational Geosciences 23, 149–168 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  53. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. The MIT Press (2016)

    MATH  Google Scholar 

  54. Han, J., Haihong, E., Le, G., Du, J.: Survey on NoSQL database. In: 2011 6th international conference on pervasive computing and applications, pp. 363–366. IEEE (2011)

    Google Scholar 

  55. de Holanda, R.W., Gildin, E., Jensen, J.L.: A generalized framework for capacitance resistance models and a comparison with streamline allocation factors. Journal of Petroleum Science and Engineering 162, 260–282 (2018)

    Article  Google Scholar 

  56. Isert, C., Schwan, K.: ACDS: Adapting computational data streams for high performance. In: Proceedings 14th International Parallel and Distributed Processing Symposium. IPDPS 2000, pp. 641–646. IEEE (2000)

    Chapter  Google Scholar 

  57. Jammoul, M., Ganis, B., Wheeler, M.: Effect of reservoir properties on interwell stress interference. In: 52nd US Rock Mechanics/Geomechanics Symposium. American Rock Mechanics Association (2018)

    Google Scholar 

  58. Jammoul, M., Ganis, B., Wheeler, M.: General semi-structured discretization for flow and geomechanics on diffusive fracture networks. In: SPE Reservoir Simulation Conference. Society of Petroleum Engineers (2019)

    Google Scholar 

  59. Jammoul, M., Wheeler, M.F.: Modeling energized and foam fracturing using the phase field method. Unconventional Resources Technology Conference (URTEC) (2020)

    Google Scholar 

  60. Jenny, P., Lee, S., Tchelepi, H.: Multi-scale finite-volume method for elliptic problems in subsurface flow simulation. Journal of Computational Physics 187(1), 47–67 (2003)

    Article  MATH  Google Scholar 

  61. Klie, H.: Unlocking fast reservoir predictions via nonintrusive reduced-order models. In: SPE Reservoir Simulation Symposium, 163584-MS. Society of Petroleum Engineers, The Woodlands, TX (2013)

    Google Scholar 

  62. Klie, H., Agreda, A., Likanapaisal, P.: Optimal learning of field operations and well placement in the presence of uncertainty. In: International Petroleum Technology Conference. SPE (2015)

    Google Scholar 

  63. Klie, H., Bangerth, W., Gai, X., Wheeler, M.F., Stoffa, P.L., Sen, M., Parashar, M., Catalyurek, U., Saltz, J., Kurc, T.: Models, methods and middleware for grid-enabled multiphysics oil reservoir management. Engineering with Computers 22(3–4), 349–370 (2006)

    Article  Google Scholar 

  64. Klie, H., Chen, H., Wang, Q., Willcox, K.: Enabling optimal production strategies under uncertainties with the aid of non-intrusive model reduction methods. In: European Conference on the Mathematics of Oil Recovery. EAGE, Biarritz, France (2012)

    Google Scholar 

  65. Klie, H., Florez, H.: Data-driven prediction of unconventional shale-reservoir dynamics. SPE Journal August (2020)

    Google Scholar 

  66. Klie, H., Yan, B., Klie, A.: Transfer learning for scalable optimization of unconventional field operations. In: Unconventional Resources Technology Conference. SPE/AAPG/SEG (2020)

    Google Scholar 

  67. Kurc, T., Catalyurek, U., Zhang, X., Saltz, J., Martino, R., Wheeler, M., Peszyńska, M., Sussman, A., Hansen, C., Sen, M.: A simulation and data analysis system for large-scale, data-driven oil reservoir simulation studies. Concurrency and Computation: Practice and Experience 17(11), 1441–1467 (2005)

    Article  Google Scholar 

  68. Kurtzer, G.M., Sochat, V., Bauer, M.W.: Singularity: Scientific containers for mobility of compute. PloS one 12(5), e0177459 (2017)

    Article  Google Scholar 

  69. Lee, J.A., Verleysen, M.: Nonlinear Dimensionality Reduction, 1st edn. Springer Publishing Company, Incorporated (2007)

    Book  MATH  Google Scholar 

  70. Lee, K., Carlberg, K.T.: Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders. Journal of Computational Physics 404, 108973 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  71. Lee, S., Wolfsteiner, C., Tchelepi, H.: Multiscale finite-volume formulation for multiphase flow in porous media: black oil formulation of compressible, three phase flow with gravity. Computational Geosciences 12, 351–366 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  72. Li, C.S., Darema, F., Chang, V.: Distributed behavior model orchestration in cognitive internet of things solution. Enterprise Information Systems 12(4), 414–434 (2018)

    Article  Google Scholar 

  73. Li, C.S., Darema, F., Kantere, V., Chang, V.: Orchestrating the cognitive internet of things. In: The first international conference on Internet of Things and Big Data (22/04/16–25/04/16) (2016). https://eprints.soton.ac.uk/390192/

  74. Li, J., Wheeler, M.F.: Uniform convergence and superconvergence of mixed finite element methods on anisotropically refined grids. SIAM Journal on Numerical Analysis 38(3), 770–798 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  75. Lotfollahi, M., Kim, I., Beygi, M.R., Worthen, A.J., Huh, C., Johnston, K.P., Wheeler, M.F., DiCarlo, D.A.: Foam generation hysteresis in porous media: Experiments and new insights. Transport in Porous Media 116(2), 687–703 (2017)

    Article  Google Scholar 

  76. Mann, V., Matossian, V., Muralidhar, R., Parashar, M.: Discover: An environment for web-based interaction and steering of high-performance scientific applications. Concurrency and Computation: Practice and Experience 13(89), 737–754 (2001)

    Article  MATH  Google Scholar 

  77. Mann, V., Parashar, M.: Engineering an interoperable computational collaboratory on the grid. Concurrency and Computation: Practice and Experience 14(13–15), 1569–1593 (2002)

    Article  MATH  Google Scholar 

  78. Matossian, V., Parashar, M.: Enabling peer-to-peer interactions for scientific applications on the grid. In: European Conference on Parallel Processing, pp. 1240–1247. Springer, Berlin, Heidelberg (2003)

    Google Scholar 

  79. Merkel, D.: Docker: lightweight Linux containers for consistent development and deployment. Linux journal 2014(239), 2 (2014)

    Google Scholar 

  80. Min, B., Sun, A.Y., Wheeler, M.F., Jeong, H.: Utilizationofmultiobjectiveoptimization for pulse testing dataset from a CO2-EOR/sequestration field. Journal of Petroleum Science and Engineering 170, 244–266 (2018)

    Article  Google Scholar 

  81. Mohaghegh, S.D.: Data-driven reservoir modeling. SPE (2017)

    Google Scholar 

  82. Muralidhar, R., Parashar, M.: A distributed object infrastructure for interaction and steering. Concurrency and Computation: Practice and Experience 15(10), 957–977 (2003)

    Article  MATH  Google Scholar 

  83. Narayanan, S., Catalyurek, U., Kurc, T., Zhang, X., Saltz, J.: Applying database support for large scale data driven science in distributed environments. In: Proceedings. First Latin American Web Congress, pp. 141–148. IEEE (2003)

    Chapter  Google Scholar 

  84. Ngom, B., Diallo, M., Marilleau, N.: Medart-mas: Meta-model of data assimilation on real-time multi-agent simulation. In: 2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), pp. 1–7 (2020)

    Google Scholar 

  85. Oldfield, R., Kotz, D.: Armada: A parallel file system for computational grids. In: Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid, pp. 194–201. IEEE (2001)

    Chapter  Google Scholar 

  86. Oliver, D.S., Chen, Y.: Recent progress on reservoir history matching: a review. Computational Geosciences 15(1), 185–221 (2011)

    Article  MATH  Google Scholar 

  87. Parashar, M., Klie, H., Catalyurek, U., Kurc, T., Bangerth, W., Matossian, V., Saltz, J., Wheeler, M.F.: Application of grid-enabled technologies for solving optimization problems in data-driven reservoir studies. Future Generation Computer Systems 21(1), 19–26 (2005)

    Article  Google Scholar 

  88. Parashar, M., Liu, H., Li, Z., Matossian, V., Schmidt, C., Zhang, G., Hariri, S.: Automate: Enabling autonomic applications on the grid. Cluster Computing 9(2), 161–174 (2006)

    Article  Google Scholar 

  89. Parashar, M., Muralidhar, R., Lee, W., Arnold, D., Dongarra, J., Wheeler, M.: Enabling interactive and collaborative oil reservoir simulations on the grid. Concurrency and Computation: Practice and Experience 17(11), 1387–1414 (2005)

    Article  Google Scholar 

  90. Parashar, M., Simonet, A., Rodero, I., Ghahramani, F., Agnew, G., Jantz, R., Honavar, V.: The Virtual Data Collaboratory: A Regional Cyberinfrastructure for Collaborative Data-Driven Research. Computing in Science Engineering 22(3), 79–92 (2020). https://doi.org/10.1109/MCSE.2019.2908850

    Article  Google Scholar 

  91. Parashar, M., Von Laszewski, G., Verma, S., Gawor, J., Keahey, K., Rehn, N.: A CORBA commodity grid kit. Concurrency and Computation: Practice and Experience 14(13–15), 1057–1074 (2002)

    Article  MATH  Google Scholar 

  92. Peszyńska, M., Wheeler, M.F., Yotov, I.: Mortar upscaling for multiphase flow in porous media. Computational Geosciences 6(1), 73–100 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  93. Ping, J., Al-Hinai, O., Wheeler, M.F.: Data assimilation method for fractured reservoirs using mimetic finite differences and ensemble Kalman filter. Computational Geosciences 21(4), 781–794 (2017)

    Article  MathSciNet  Google Scholar 

  94. Powell, W., Ryzhov, I.: Nonlinear Dimensionality Reduction. Wiley (2012)

    Google Scholar 

  95. Renart, E.G., Balouek-Thomert, D., Parashar, M.: An edge-based framework for enabling data-driven pipelines for IoT systems. In: 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 885–894 (2019)

    Google Scholar 

  96. Rommelse, J.R.: Data assimilation in reservoir management. Ph.D. thesis, Delft University of Technology (2009)

    Google Scholar 

  97. Rousset, M., Huang, C.K., Klie, H., Durlofsky, L.: Reduced-order modeling for thermal recovery processes. Computational Geosciences 18(3–4), 401–415 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  98. Rowley, C.W., Dawson, S.T.: Model reduction for flow analysis and control. Annual Review of Fluid Mechanics 49(1), 387–417 (2017). https://doi.org/10.1146/annurev-fluid-010816-060042

    Article  MathSciNet  MATH  Google Scholar 

  99. Sen, M.K., Stoffa, P.L.: Global optimization methods in geophysical inversion. Cambridge University Press (2013)

    Book  MATH  Google Scholar 

  100. Spall, J.C.: Introduction to stochastic search and optimization: estimation, simulation, and control. John Wiley & Sons (2003)

    Book  MATH  Google Scholar 

  101. Stonebraker, M., Brown, P., Poliakov, A., Raman, S.: The architecture of SciDB. In: International Conference on Scientific and Statistical Database Management, pp. 1–16. Springer (2011)

    Google Scholar 

  102. Subedi, P., Davis, P., Duan, S., Klasky, S., Kolla, H., Parashar, M.: Stacker: an autonomic data movement engine for extreme-scale data staging-based in-situ workflows. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, p. 73. IEEE Press (2018)

    Google Scholar 

  103. Subedi, P., Davis, P.E., Parashar, M.: Leveraging machine learning for anticipatory data delivery in extreme scale in-situ workflows. In: 2019 IEEE International Conference on Cluster Computing (CLUSTER), pp. 1–11. IEEE (2019)

    Google Scholar 

  104. Tavakoli, R., Pencheva, G., Wheeler, M.F., Ganis, B.: A parallel ensemble based framework for reservoir history matching and uncertainty characterization. Computational Geosciences 17(1), 83–97 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  105. Tavakoli, R., Srinivasan, S., Wheeler, M.F.: Rapid updating of stochastic models by use of an ensemble-filter approach. SPE Journal 19(03), 500–513 (2014)

    Article  Google Scholar 

  106. Teodoro, G., Pan, T., Kurc, T., Kong, J., Cooper, L., Klasky, S., Saltz, J.: Region templates: Data representation and management for high-throughput image analysis. Parallel Computing 40(10), 589–610 (2014)

    Article  Google Scholar 

  107. Thönes, J.: Microservices. IEEE Software 32(1), 116–116 (2015)

    Article  Google Scholar 

  108. Van Leeuwen, P.J., Evensen, G.: Data assimilation and inverse methods in terms of a probabilistic formulation. Monthly Weather Review 124(12), 2898–2913 (1996)

    Article  Google Scholar 

  109. Wang, Z., Subedi, P., Duan, S., Qin, Y., Davis, P., Simonet, A., Rodero, I., Parashar, M.: Exploring trade-offs in dynamic task triggering for loosely coupled scientific workflows. arXiv preprint arXiv:2004.10381 (2020)

    Google Scholar 

  110. Wheeler, M.F., Peszyńska, M.: Computational engineering and science methodologies for modeling and simulation of subsurface applications. Advances in Water Resources 25(8–12), 1147–1173 (2002)

    Article  Google Scholar 

  111. Wheeler, M.F., Yotov, I.: Physical and computational domain decompositions for modeling subsurface flows. Contemporary Mathematics 218, 217–228 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  112. Wheeler, M.F., Yotov, I., Ganis, B., Pencheva, G., Al Hinai, O., Lee, S., Min, B., Ping, J., Singh, G., Almani, T., Dana, S., Jammoul, M., White, D., Zunino, P., Ambartsumyan, I., Khattatov, E., Nguyen, T., Song, P., Tanase, R., Wang, C., Zakerzadeh, R.: Multiscale modeling and simulation of multiphase flow in porous media coupled with geomechanics (final report) (2019). https://doi.org/10.2172/1509810

  113. White, T.: Hadoop: The definitive guide. O’Reilly Media, Inc. (2012)

    Google Scholar 

  114. Williams, G., Mansfield, M., MacDonald, D., Bush, M.: Top-down reservoir modelling. In: SPE annual technical conference and exhibition. Society of Petroleum Engineers (2004)

    Google Scholar 

  115. Wu, R., Liu, B., Chen, Y., Blasch, E., Ling, H., Chen, G.: A container-based elastic cloud architecture for pseudo real-time exploitation of wide area motion imagery (wami) stream. Journal of Signal Processing Systems 88(2), 219–231 (2017)

    Article  Google Scholar 

  116. Yu, J., Wu, J., Sarwat, M.: Geospark: A cluster computing framework for processing large-scale spatial data. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–4 (2015)

    Google Scholar 

  117. Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: Cluster computing with working sets. HotCloud 10(10–10), 95 (2010)

    Google Scholar 

  118. Zhang, D.: Stochastic methods for flow in porous media: coping with uncertainties. Elsevier (2001)

    Google Scholar 

  119. Zhang, L., Parashar, M.: A dynamic geometry-based shared space interaction framework for parallel scientific applications. In: International Conference on High-Performance Computing, pp. 189–199. Springer (2004)

    Google Scholar 

  120. Zhang, L., Parashar, M.: Seine: a dynamic geometry-based shared-space interaction framework for parallel scientific applications. Concurrency and Computation: Practice and Experience 18(15), 1951–1973 (2006)

    Article  Google Scholar 

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Acknowledgments

The authors would like to thank Dr. Federica Darema for her determined and visionary leadership in promoting the scientific frontiers of DDDAS and for providing the authors with opportunities to growth in this direction.

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Authors and Affiliations

  1. Scientific Computing and Imaging Institute and School of Computing, The University of Utah, Salt Lake City, UT, USA

    M. Parashar

  2. Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA

    Tahsin Kurc & Joel H. Saltz

  3. DeepCast.ai, Houston, TX, USA

    H. Klie

  4. Center for Subsurface Modeling, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA

    M. F. Wheeler, M. Jammoul & R. Dong

Authors
  1. M. Parashar
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  2. Tahsin Kurc
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  3. H. Klie
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  4. M. F. Wheeler
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  5. Joel H. Saltz
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  6. M. Jammoul
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  7. R. Dong
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Corresponding author

Correspondence to M. Parashar .

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Editors and Affiliations

  1. InfoSymbiotic Systems Society, Bethesda, MD, USA

    Frederica Darema

  2. MOVEJ Analytics, Dayton, OH, USA

    Erik P. Blasch

  3. Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sai Ravela

  4. Information Directorate, Air Force Laboratory, Rome, NY, USA

    Alex J. Aved

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Parashar, M. et al. (2023). Dynamic Data-Driven Application Systems for Reservoir Simulation-Based Optimization: Lessons Learned and Future Trends. In: Darema, F., Blasch, E.P., Ravela, S., Aved, A.J. (eds) Handbook of Dynamic Data Driven Applications Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-27986-7_11

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  • DOI: https://doi.org/10.1007/978-3-031-27986-7_11

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