Effective geo-system management involves understanding of the interplay between surface entities (e.g., locations of injection and production wells in an oil reservoir) and appropriately effecting subsurface characteristics. This in turn requires efficient integration of complex numerical models of the environment, optimization procedures, and decision making processes. The dynamic, data-driven application systems (DDDAS) paradigm offers a promising framework to address this requirement. To achieve this goal, we have developed advanced multi-physics, multi-scale, and multi-block numerical models and autonomic systems software for dynamic, data-driven applications systems. This work has enabled a new generation of data-driven, interactive and dynamically adaptive strategies for subsurface characterization and management. These strategies have been applied to different aspects of subsurface management in strategically important application areas, including simulation-based optimization for the optimal oil well placement and the data-driven management of the Ruby Gulch Waste Repository. This paper summarizes the key outcomes and achievements of our work, as well as reports ongoing and future activities focused on uncertainty estimation and characterization.
References
1.
BangerthW., An Autonomic Reservoir Framework for the Stochastic Optimization of Well Placement. Cluster Computing, 2005. 8(4): P. 255–269.
2.
BangerthW., On Optimization Algorithms for the Reservoir Oil Well Placement Problem. Computational Geosciences, 2006. 10(3): P. 303–319.
3.
BangerthW., An Autonomic Reservoir Framework for the Stochastic Optimization of Well Placement. Cluster Computing: The Journal of Networks, Software Tools, and Applications, 2005. 8(4): P. 255–269.
4.
KlieH., Models, methods and middleware for Grid-enable multiphysics oil reservoir management. Engineering with Computers, 2006. 22: P. 349–370.
5.
MatossianV., Autonomic Oil Reservoir Optimization on the Grid. Concurrency and Computation: Practice and Experience, John Wiley and Sons, 2005. 17(1): P. 1–26.
6.
ParasharM., Application of grid-enabled technologies for solving optimization problems in data-driven reservoir studies. Future Generation of Computer Systems, 2005. 21(1): P. 19–26.
7.
ParasharM., Application of Grid-Enabled Technologies for Solving Optimization Problems in Data-Driven Reservoir Studies. Journal of Future Generation Computer System, Special Issue on Engineering Autonomic Systems, 2005. 21(1): P. 19–26.
8.
ParasharM., Enabling Interactive Oil Reservoir Simulations on the Grid. Concurrency and Computation: Practice and Experience, 2005. 17(11): P. 1387–1414.
9.
MannV. and ParasharM., DISCOVER: A Computational Collaboratory for Interactive Grid Applications, in Grid Computing: Making the Global Infrastructure a Reality, BermanF.FoxG., and HeyT., Editors. 2003, John Wiley and Sons. p. 727–744.
10.
ParasharM., AutoMate: Enabling Autonomic Grid Applications. Cluster Computing: The Journal of Networks, Software Tools, and Applications, Special Issue on Autonomic Computing, 2006. 9(2): P. 161–174.
11.
BeynonM., Processing Large-Scale Multidimensional Data in Parallel and Distributed Environments. Parallel Computing, 2002. 28(5): P. 827–859.
12.
BeynonM., DataCutter: Middleware for Filtering Very Large Scientific Datasets on Archival Storage Systems, in Proceedings of the Eighth Goddard Conference on Mass Storage Systems and Technologies/ 17th IEEE Symposium on Mass Storage Systems.2000: College Park, MD.
13.
BeynonM., Distributed Processing of Very Large Datasets with DataCutter. Parallel Computing, 2001. 27(11): P. 1457–2478.
14.
BeynonM.D., Efficient Manipulation of Large Datasets on Heterogeneous Storage Systems, in Proceedings of the 11th Heterogeneous Computing Workshop (HCW2002).2002, IEEE Computer Society Press.
15.
NarayananS., Applying Database Support for Large Scale Data Driven Science in Distributed Environments, in Proceedings of the Fourth International Workshop on Grid Computing (Grid 2003).2003: Phoenix, Arizona. p. 141–148.
16.
NarayananS., Database Support for Data-driven Scientific Applications in the Grid. Parallel Processing Letters, 2003. 13(2): P. 245–273.
17.
SaltzJ., Driving Scientific Applications by Data in Distributed Environments, in Proceedings of Workshop on Dynamic Data Driven Application Systems (International Conference on Computational Science).2003, Springer-Verlag.
18.
WengL., An Approach for Automatic Data Virtualization, in Proceedings of the 13th IEEE International Symposium on High-Performance Distributed Computing (HPDC-13).2004: Honolulu, Hawaii. p. 24–33.
19.
ZhangL. and ParasharM., Seine: A Dynamic Geometry-based Shared Space Interaction Framework for Parallel Scientific Applications. Concurrency and Computations: Practice and Experience, 2006. 18(15): P. 1951–1973.
20.
WheelerM.F.PeszynskaM., and DawsonC.N.. Multiphysics couplings for environmental problems. in Proceedings of the DOD Users Group Conference.2000. Albuquerque, New Mexico.
21.
KlieH. and WheelerM.F.. Krylov-Secant Methods for Accelerating the Solution of Fully Implicit formulations. in SPE Reservoir Simulation Symposium.2005: SPE paper No. 92863, Houston, Texas.
22.
KlieH.Deflation AMG solvers for highly ill-conditioned reservoir simulation problems. in SPE Reservoir Symulation Symposium.2007. Houston, Texas: SPE paper No. 105820.
23.
StubenK.Algebraic Multigrid Methods (AMG) for the Efficient Solution of Fully Implicit Formulations in Reservoir Simulation. in SPE Reservoir Symulation Symposium.2007. Houston, Texas: SPE paper No. 105832.
24.
WheelerM.F. and YotovI., A multipoint flux mixed finite element method. SIAM J. Num. Anal., 2006. 44(5): P. 2082–2106.
25.
WheelerM.F. and YotovI., A posteriori error estimates for the mortar mixed finite element method. SIAM J. Numer. Anal., 2005. 43(3): P. 1021–1042.
26.
BanchsR.E., A neural stochastic multiscale optimization framework for sensor-based parameter estimation. Integrated Computer-Aided Engineering, 2007. 14(3): P. 213–223.
27.
KlieH.Assessing the Value of Sensor Information in 4-D Seismic History Matching. in 76th SEG International Exposition & Annual Meeting.2006. New Orleans.
28.
RodriguezA.Assessing Multiple Resolution Scales in History Matching with Stochastic Neural Metamodels. in SPE Reservoir Symulation Symposium.2007. Houston, Texas: SPE paper No. 105820.
29.
LiuH. and ParasharM., Accord: A Programming Framework for Autonomic Applications. IEEE Transactions on Systems, Man and Cybernetics, Special Issue on Engineering Autonomic Systems, 2006. 36(3): P. 341–352.
30.
ChandraS., Investigating Autonomic Runtime Management Strategies for SAMR Applications. International Journal of Parallel Programming, 2005. 33(2–3): P. 247–259.
31.
KlieH.Parallel well location optimization using stochastic algorithms on the grid computational framework. in 9th European Conference on the Mathematics of Oil Recovery (ECMOR). 0030: EAGE.
32.
ParasharM.Towards dynamic data-driven management of the Ruby Gulch Waste Repository. in 6th International Conference Computational Science.2006. Reading, UK: Springer Verlag.
33.
VersteegR., A structured approach to the use of near-surface geophysics in long-term monitoring. Expert Systems with Applications, 2004. 23(7): P. 700–703.
34.
GildinE.Projection-based Approximation Methods for the Optimal Control of Smart Fields. in 10th European Conference on the Mathematics of Oil Recovery (ECMOR). 0004: EAGE.
35.
KlieH. and WheelerM.F.. An Efficient Krylov-Karhunen-Loeve Subspace Projection Method for Stochastic Finite Element Analysis of Flow in Highly Heterogeneous Porous Media. in MAFELAP.2006. Brunel University, UK.
36.
LiH. and ZhangD., Probabilistic Collocation Method for Flow in Porous Media: Comparisons with other Stochastic Methods.Water Resources Research, submitted, 2007.
37.
XiuD., Efficient collocational approach for parametric uncertainty analysis. Communications in Computational Physics, 2007. 2(2): P. 293–309.
38.
EvensenG., Data Assimilation: The Ensemble Kalman Filter.2007: Springer.
39.
RisticB.ArulampalamS., and GordonN., Beyond the Kalman Filter: Particle Filters for Tracking Applications.2004, London: Artech House.
40.
PearsonR., Imperfect Data: Dealing with Contamination and Incomplete Records.2005: SIAM.
