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Models, methods and middleware for grid-enabled multiphysics oil reservoir management

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Abstract

Meeting the demands for energy entails a better understanding and characterization of the fundamental processes of reservoirs and of how human made objects affect these systems. The need to perform extensive reservoir studies for either uncertainty assessment or optimal exploitation plans brings up demands of computing power and data management in a more pervasive way. This work focuses on high performance numerical methods, tools and grid-enabled middleware systems for scalable and data-driven computations for multiphysics simulation and decision-making processes in integrated multiphase flow applications. The proposed suite of tools and systems consists of (1) a scalable reservoir simulator, (2) novel stochastic optimization algorithms, (3) decentralized autonomic grid middleware tools, and (4) middleware systems for large-scale data storage, querying, and retrieval. The aforementioned components offer enormous potential for performing data-driven studies and efficient execution of complex, large-scale reservoir models in a collaborative environment.

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References

  1. Foster I, Kesselman C (eds) (1999) Globus: a toolkit based grid architecture. Morgan Kaufman, San Francisco, pp 259–278

  2. Frey J, Tannenbaum T, Foster I, Livny M, Tuecke S (2001) Condor-G: a computation management agent for multi-institutional grids. In: Proceedings of the 10th IEEE symposium on high performance distributed computing (HPDC10). IEEE Press, New York

  3. Rajasekar A, Wan M, Moore R (2002) MySRB & SRB—components of a data grid. In: The 11th international symposium on high performance distributed computing (HPDC-11)

  4. Wolski R, Spring N, Hayes J (1999) The Network Weather Service: a distributed resource performance forecasting service for metacomputing. J Future Gener Comput Syst 15(5–6):757–768

    Article  Google Scholar 

  5. Beynon MD, Kurc T, Catalyurek U, Chang C, Sussman A, Saltz J (2001) Distributed processing of very large datasets with DataCutter. Parallel Comput 27(11):1457–1478

    Article  MATH  Google Scholar 

  6. Grimshaw AS, Wulf WA, the Legion Team (1997) The legion vision of a worldwide virtual computer. Commun ACM 40(1):39–45

    Google Scholar 

  7. Allen G, Dramlitsch T, Foster I, Karonis N, Ripeanu M, Seidel E, Toonen B (2001) Supporting efficient execution in heterogeneous distributed computing environments with Cactus and Globus. In: Proceedings of the ACM/IEEE SC1001 conference. ACM Press, New York

  8. Common Component Architecture Forum. http://www.cca-forum.org

  9. Allcock W, Chervenak A, Foster I, Kesselman C, Salisbury C, Tuecke S (2001) The DataGrid: towards an architecture for the distributed management and analysis of large scientific datasets. J Netw Comput Appl 23:187–200

    Google Scholar 

  10. Casanova H, Dongarra J (1998) Applying Netsolve’s network-enabled server. IEEE Comput Sci Eng 5(3):57–67

    Article  Google Scholar 

  11. Czajkowski K, Fitzgerald S, Foster I, Kesselman C (2001) Grid Information services for distributed resource sharing. In: 10th IEEE symposium on high performance distributed computing

  12. Oldfield R, Kotz D (2001) Armada: a parallel file system for computational grids. In: Proceedings of CCGrid2001: IEEE international symposium on cluster computing and the grid. IEEE Computer Society Press, Brisbane, Australia

  13. Sato M, Nakada H, Sekiguchi S, Matsuoka S, Nagashima U, Takagi H (1997) Ninf: a network based Information Library for a global world-wide computing infrastructure. In: Proceedings of HPCN’97 (LNCS-1225), pp 491–502

  14. Thain D, Basney J, Son S, Livny M (2001) Kangaroo approach to data movement on the grid. In: Proceedings of the 10th IEEE symposium on high performance distributed computing (HPDC10)

  15. Thain D, Bent J, Arpaci-Dusseau A, Arpaci-Dusseau R, Livny M (2001) Gathering at the well: creating communities for grid I/O. In: Proceedings of supercomputing 2001. Denver, CO, USA

  16. Vazhkudai S, Tuecke S, Foster I (2001) Replica selection in the Globus data grid. In: International workshop on data models and databases on Clusters and the grid (DataGrid 2001). IEEE Computer Society Press, New York

  17. Bevc D (2003) eBusiness and geophysics, vol 22. The Leading Edge, Provo, pp 53–53

  18. Bevc D, Popovici M (2003) Integrated Internet collaboration, vol 22. The Leading Edge, Provo, pp 54–57

  19. Fuller J, Fay J (2003) How the Internet is influencing today’s E&P business, vol 22. The Leading Edge, Provo, pp 65–68

  20. Hanley S (2003) The collaborative power of IT leads industry transformation, vol 22. The Leading Edge, Provo, pp 62–64

  21. Karbarz F (2003) Grid computing for seismic processing, vol 22. The Leading Edge, Provo, pp 58–60

  22. Bangerth W, Matossian V, Parashar M, Klie H, Wheeler M (2005) An autonomic reservoir framework for the stochastic optimization of well placement. Cluster Comput J Netw Softw Tools 8(4):255–269

    Article  Google Scholar 

  23. Klie H, Bangerth W, Wheeler MF, Parashar M, Matossian V (2004) Parallel well location optimization using stochastic algorithms on the grid computational framework. In: 9th European conference on the mathematics of oil recovery (ECMOR). EAGE, August 30–September 2 2004

  24. Matossian V, Bhat V, Parashar M, Peszynska M, Sen M, Stoffa P, Wheeler MF (2005) Autonomic oil reservoir optimization on the grid. Concur Comput Pract Exp 17(1):1–26

    Article  Google Scholar 

  25. Parashar M, Klie H, Catalyurek U, Kurc T, Bangerth W, Matossian V, Saltz J, Wheeler MF (2005) Application of grid-enabled technologies for solving optimization problems in data-driven reservoir studies. J Future Gener Comput Syst Spec Issue Eng Auton Syst 21(1):19–26

    Google Scholar 

  26. Ingber L (1989) Very fast simulated reannealing. Math Comput Model 12:967–993

    Article  MATH  MathSciNet  Google Scholar 

  27. Sen M, Stoffa P (1995) Global optimization methods in geophysical inversion. Elsevier, Amsterdam

    MATH  Google Scholar 

  28. Spall JC (1992) Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Trans Autom Control 37:332–341

    Article  MATH  MathSciNet  Google Scholar 

  29. Spall JC (2003) Introduction to stochastic search and optimization: Estimation, simulation and control. Wiley, New Jersey

    Book  MATH  Google Scholar 

  30. Gai X, Dean R, Wheeler MF, Liu R (2003) Coupled geomechanical and reservoir modeling on parallel computers. In: SPE 79700, proceedings of SPE reservoir symposium, Houston, TX

  31. Lu Q, Peszyńska M, Wheeler MF (2001) A parallel multi-block black-oil model in multi-model Implementation. In: 2001 SPE reservoir simulation symposium, Houston, TX, SPE 66359

  32. Minkoff S, Stone CM, Arguello JG, Bryant S, Eaton J, Peszynska M, Wheeler MF (1999) Staggered in time coupling of reservoir flow simulation and geomechanical deformation: Step 1—one-way coupling. In: 1999 SPE symposium on reservoir simulation, Houston, TX, SPE 51920

  33. Parashar M, Wheeler JA, Pope G, Wang K, Wang P (1997) A new generation EOS compositional reservoir simulator. Part II: framework and multiprocessing. In: 14th SPE symposium on reservoir simulation, Dallas, TX, Society of Petroleum Engineers, pp 31–38

  34. Peszyńska M, Lu Q, Wheeler MF (2000) Multiphysics coupling of codes. In: Bentley LR, Sykes JF, Brebbia CA, Gray WG, Pinder GF (eds) Computational methods in water resources. A. A. Balkema, Amsterdam, pp 175–182

    Google Scholar 

  35. Peszyńska M, Wheeler MF, Yotov I (2002) Mortar upscaling for multiphase flow in porous media. Comput Geosci 6(1):73–100

    Article  MathSciNet  Google Scholar 

  36. Wang P, Yotov I, Wheeler MF, Arbogast T, Dawson CN, Parashar M, Sepehrnoori K (1997) A new generation EOS compositional reservoir simulator. Part I: formulation and discretization. In: 14th SPE symposium on reservoir simulation, Dallas, TX, Society of Petroleum Engineers, pp 55–64

  37. Wheeler MF (2002) Advanced techniques and algorithms for reservoir simulation, II: the multiblock approach in the integrated parallel accurate reservoir simulator (IPARS). In: Chadam J, Cunningham A, Ewing RE, Ortoleva P, Wheeler MF (eds) IMA volumes in mathematics and its applications, vol 131. Resource recovery, confinement, and remediation of environmental hazards. Springer, Berlin Heidelberg New York

  38. Wheeler MF, Peszynska M (2002) Computational engineering and science methodologies for modeling and simulation of subsurface applications. Adv Water Resource 25(8):1147–1173

    Google Scholar 

  39. Wheeler MF, Wheeler JA, Peszyńska M (2000) A distributed computing portal for coupling multi-physics and multiple domains in porous media. In: Bentley LR, Sykes JF, Brebbia CA, Gray WG, Pinder GF (eds) Computational methods in water resources. A. A. Balkema, Amsterdam, pp 167–174

    Google Scholar 

  40. Arbogast T, Cowsar LC, Wheeler MF, Yotov I (2000) Mixed finite element methods on non-matching multiblock grids. SIAM J Numer Anal 37:1295–1315

    Article  MATH  MathSciNet  Google Scholar 

  41. Lu Q (2000) A parallel multi-block/multi-physics approach for multi-phase flow in porous media. PhD Thesis, University of Texas at Austin

  42. Narayanan S, Catalyurek U, Kurc T, Zhang X, Saltz J (2003) Applying Database support for large scale data driven science in distributed environments. In: Proceedings of the 4th international workshop on grid computing (Grid 2003), Phoenix, Arizona, pp 141–148

  43. Saltz J et al (2003) Driving scientific applications by data in distributed environments. In: Dynamic data driven application systems workshop, held jointly with ICCS 2003, Melbourne, Australia

  44. Parashar M, Liu H, Li Z, Matossian V, Schmidt C, Zhang G, Hariri S (2006) AutoMate: enabling autonomic grid applications. Cluster Comput J Netw Softw Tools Appl Spec Issue Auton Comput 9(2):161–174

    Google Scholar 

  45. Lacroix S, Vassileski Y, Wheeler J, Wheeler M (2003) Iterative solution methods for modeling multiphase flow in porous media fully implicitly. SIAM J Sci Comput 25(3):905–926

    Article  MATH  MathSciNet  Google Scholar 

  46. Li J, Wheeler MF (2000) Uniform convergence and superconvergence of mixed finite element methods on anisotropically refined grids. SIAM J Numer Anal 38(3):770–798

    Article  MATH  MathSciNet  Google Scholar 

  47. Peszyńska M, Lu Q, Wheeler MF (1999) Coupling different numerical algorithms for two phase fluid flow. In: Whiteman JR (ed) MAFELAP Proceedings of mathematics of finite elements and applications. Brunel University, Uxbridge, UK, pp 205–214

  48. Wheeler MF, Yotov I (1998) Physical and computational domain decompositions for modeling subsurface flows. In: Mandel J et al (eds) 10th international conference on domain decomposition methods, contemporary mathematics, vol 218. American Mathematical Society, pp 217–228

  49. Wheeler MF, Arbogast T, Bryant S, Eaton J, Lu Q, Peszyńska M, Yotov I (1999) A parallel multiblock/multidomain approach to reservoir simulation. In: 15th SPE symposium on reservoir simulation, Houston, TX. Society of Petroleum Engineers. SPE 51884, pp 51–62

  50. Wohlmuth BI (2000) A mortar finite element method using dual spaces for the Lagrange multiplier. SIAM J Numer Anal 38:989–1012

    Article  MATH  MathSciNet  Google Scholar 

  51. Yotov I (1996) Mixed finite element methods for flow in porous media. PhD Thesis, Rice University, Houston, TX. TR96-09, Dept. Comp. Appl. Math., Rice University and TICAM report 96-23, University of Texas at Austin

  52. Wheeler MF, Yotov I (2005) A posteriori error estimates for the mortar mixed finite element method. SIAM J Numer Anal 43(3):1021–1042

    Article  MATH  MathSciNet  Google Scholar 

  53. Peszynska M, Sun S (2002) Reactive transport model coupled to multiphase flow models. In: Hassanizadeh SM, Schotting RJ, Gray WG, Pinder GF (eds) Computational methods in water resources. Elsevier, Amsterdam, pp 923–930

    Google Scholar 

  54. Snir M, Otto S, Huss-Lederman S, Walker D, Dongarra J (1996) MPI: the complete reference. MIT Press, New York

    Google Scholar 

  55. Carriero N, Gelernter D (1989) Linda in context. Commun ACM 32(0001–0782):444–458

    Article  Google Scholar 

  56. Sterck H, Markel R, Pohl T, Rude U (2003) A lightweight Java taskspaces framework for scientific computing on compuatational grids. In: Proceedings of the 18th annual ACM symposium on applied computing, 1-58113-624-2, Melbourne, FL, USA, pp 1024–1030

  57. Zhang L, Parashar M (2004) A dynamic geometry-based shared space interaction framework for parallel scientific applications. In: Proceedings of the 11th annual international conference on high performance computing (HiPC 2004), vol 3296. LNCS, Springer, Bangalore, pp 189–199

  58. Bourbie T, O OC, Zinsner B (1987) Acoustics of porous media. IFP Publications, Paris

  59. Dean R, Gai X, Stone C, Minkoff S (2003) A comparison of techniques for coupling porous flow and geomechanics. In: Tthe SPE reservoir simulation symposium. Houston, TX, SPE 79709

  60. Gai X (2004) A coupled geomechanics and reservoir flow model on parallel computers. PhD Thesis, The University of Texas at Austin

  61. Kenter C, van den Beukel A, Hatchell P, Maron K, Molenaar M (2004) Geomechanics and 4D: evaluation of reservoir characteristics from time-shifts in the overburden. In: Presented at Gulf Rocks 2004, Houston, TX, June 5–9. ARMA/NARMS 04-627

  62. Minkoff S, Stone C, Arguello J, Bryant S, Eaton J, Peszyńska M, Wheeler M (1999) Coupled geomechanics and flow simulation for time-lapse seismic modeling. In: Expanded Abstracts, 1667–1670. Soc Expl Geophys

  63. Molenaar M, Hatchell P, van den Beukel A (2004) 4D in-situ stress as a complementary tool for optimizing field management. In: Presented at Gulf Rocks 2004, Houston, TX, June 5–9. ARMA/NARMS 04-639

  64. Bangerth W, Klie H, Wheeler M, Stoffa P, Sen M (2006) On optimization algorithms for the reservoir oil well placement problem. Comput Geosci (in press)

  65. Ji XD, Familoni BD (1999) A diagonal recurrent neural network-based hybrid direct adaptive SPSA control system. IEEE Trans Autom Control 44:1469–1473

    Article  MATH  MathSciNet  Google Scholar 

  66. Maeda Y, Toshiki T (2003) FPGA implementation of a pulse density neural network with learning ability using simultaneous perturbation. IEEE Trans Neural Netw 14:688–695

    Article  Google Scholar 

  67. Chunduru RK, Sen MK, Stoffa PL (1997) Hybrid optimization methods for geophysical inversion. Geophysics 62:1196–1207

    Article  Google Scholar 

  68. Parashar M, Browne J (2005) Conceptual and implementation models for the grid. Proc IEEE Spec Issue Grid Comput 93(3):653–668

    Google Scholar 

  69. Liu H, Parashar M (2006) Accord: a programming framework for autonomic applications. IEEE Trans Syst Man Cybern Spec Issue Eng Auton Syst 36(3):341–352

    Google Scholar 

  70. Liu H, Parashar M, Hariri S (2004) A component-based programming framework for autonomic applications. In: the 1st IEEE international conference on autonomic computing (ICAC-04), New York, pp 10–17

  71. Liu H, Parashar M (2005) A framework for rule-based autonomic management of parallel scientific applications. In: The 2nd IEEE international conference on autonomic computing (ICAC-05), Seattle, WA, USA

  72. Liu H, Parashar M (2005) Rule-based monitoring and steering of distributed scientific applications. Int J High Perform Comput Netw 3(4):272–282

    Article  Google Scholar 

  73. Liu H, Parashar M (2005) Enabling self-management of component-based high-performance scientific applications. In: The 14th IEEE international symposium on high performance distributed computing (HPDC-14). Research Triangle Park, NC, pp 59–68

  74. Liu H, Bhat V, Parashar M, Klasky S (2005) An autonomic service architecture for self-managing grid applications. In: Proceedings of the 6th IEEE/ACM international workshop on grid computing (Grid 2005). Seattle, WA, USA

  75. Chen J, Silver D, Parashar M (2003) Real time feature extraction and tracking in a computational steering environment. In: Proceedings of the 11th high performance computing symposium (HPC 2003), Orlando, FL

  76. Liu H, Jiang L, Parashar M, Silver D (2005) Rule-based visualization in the discover computational steering collaboratory. J Future Gener Comput Syst Spec Issue Eng Auton Syst 21(1):53–59

    Google Scholar 

  77. Chandra S, Parashar M, Yang J, Zhang Y, Hariri S (2005) Investigating autonomic runtime management strategies for SAMR applications. Int J Parallel Programm 33(2-3):247–259

    Article  Google Scholar 

  78. Jiang N, Parashar M (2004) Enabling applications in sensor-based pervasive environments. In: Proceedings of BROADNETS 2004: workshop on broadband advanced sensor networks (BaseNets 2004), San Jose, CA, USA

  79. Schmidt C, Parashar M (2004) Enabling flexible queries with guarantees in P2P systems. IEEE Netw Comput Spec Issue Inform Dissem Web 8(3):19–26

    Google Scholar 

  80. Mann V, Parashar M (2003) DISCOVER: a computational collaboratory for interactive grid applications. In: Berman F, Fox G, Hey T (eds) Grid computing: making the global infrastructure a reality. Wiley, New York, pp 727–744

    Google Scholar 

  81. Parashar M, Muralidhar R, Lee W, Wheeler M, Arnold D, Dongarra J (2005) Enabling interactive oil reservoir simulations on the grid. Concur Comput Pract Exp 17(11):1387–1414

    Article  Google Scholar 

  82. Foster I, Kesselman C, Nick J, Tuecke S (2002) Grid services for distributed system integration. IEEE Comput 36(6):37–46

    Google Scholar 

  83. Foster I, Kesselman C, Nick JM, Tuecke S (2002) The physiology of the grid: an open grid services architecture for distributed systems integration. http://www.globus.org/research/papers/ogsa.pdf

  84. Graham S, Simeonov S, Boubez T, Davis D, Daniels G, Nakamura Y, Neyama R (2002) Building Web services with Java: making sense of XML, SOAP, WSDL, and UDDI. SAMS Publishing, USA

  85. Data Access and Integration Services. //http://www.cs.man.ac.uk/grid-db/documents.html

  86. Raman V, Narang I, Crone C, Haas L, Malaika S, Mukai T, Wolfson D, Baru C. Data access and management services on grid. http://www.cs.man.ac.uk/grid-db/documents.html

  87. Bell WH, Bosio D, Hoschek W, Kunszt P, McCance G, Silander M. Project Spitfite—towards grid web service databases. http://www.cs.man.ac.uk/grid-db/documents.html

  88. Smith J, Gounaris A, Watson P, Paton NW, Fernandes A, Sakellariou R. Distributed query processing on the grid. http://www.cs.man.ac.uk/grid-db/documents.html

  89. Narayanan S, Kurc T, Catalyurek U, Saltz J (2003) Database support for data-driven scientific applications in the grid. Parallel Process Lett 13(2):245–271

    Article  MathSciNet  Google Scholar 

  90. Weng L, Agrawal G, Catalyurek U, Kurc T, Narayanan S, Saltz J (2004) An approach for automatic data virtualization. In: The 13th IEEE international symposium on high-performance distributed computing (HPDC-13)

  91. Parashar M, Matossian V, Bangerth W, Klie H, Rutt B, Kurc T, Catalyurek U, Saltz J, Wheeler M (2005) Towards dynamic data-driven optimization of oil well placement. In: Sunderam V et al (eds) Proceedings of the workshop on distributed data driven applications and systems. International conference on computational science 2005 (ICCS 2005), vol 3514–3516, Springer, Berlin Heidelberg New York, pp 656–663

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Acknowledgments

The authors want to thank the National Science Foundation (NSF) for its support under the ITR grant EIA-0121523/ EIA-0120934, grants #ACI-9619020 (UC Subcontract #10152408), #EIA-0121177, #ACI-0203846, #ACI-0130437, #ANI-0330612, #ACI-9982087, #CCF-0342615, #CNS-0406386, #CNS-0426241, #ACI-9984357, #EIA −0103674, #ANI-0335244, #CNS-0305495, #CNS-0426354 and #IIS-0430826, Lawrence Livermore National Laboratory under Grant #B517095 (UC Subcontract #10184497), and grants from Ohio Board of Regents BRTTC #BRTT02-0003.

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Authors and Affiliations

  1. Center for Subsurface Modeling, The University of Texas at Austin, Austin, TX, 78712, USA

    H. Klie, X. Gai & M. F. Wheeler

  2. Institute for Geophysics, The University of Texas at Austin, Austin, TX, 78759-8500, USA

    P. L. Stoffa & M. Sen

  3. TASSL, Department of Electrical and Computing Engineering, Rutgers University, Piscataway, NJ, USA

    M. Parashar

  4. Department of Biomedical Informatics, Ohio State University, Columbus, OH, 43210, USA

    U. Catalyurek, J. Saltz & T. Kurc

  5. Department of Mathematics, Texas A&M University, College Station, TX, 77843-3368, USA

    W. Bangerth

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  1. H. Klie
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Klie, H., Bangerth, W., Gai, X. et al. Models, methods and middleware for grid-enabled multiphysics oil reservoir management. Engineering with Computers 22, 349–370 (2006). https://doi.org/10.1007/s00366-006-0035-9

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