Выходит 6 номеров в год
ISSN Печать: 2152-5080
ISSN Онлайн: 2152-5099
Indexed in
A MULTI-STAGE BAYESIAN PREDICTION FRAMEWORK FOR SUBSURFACE FLOWS
Краткое описание
We are concerned with the development of computationally efficient procedures for subsurface flow prediction that relies on the characterization of subsurface formations given static (measured permeability and porosity at well locations) and dynamic (measured produced fluid properties at well locations) data. We describe a predictive procedure in a Bayesian framework, which uses a single-phase flow model for characterization aiming at making prediction for a two-phase flow model. The quality of the characterization of the underlying formations is accessed through the prediction of future fluid flow production.
-
Ma, S. and Morrow, N., Relationships between Porosity and Permeability for Porous Rocks.
-
Sperl, J. and Trckova, J., Permeability and porosity of rocks and their relationship based on laboratory testing.
-
Jin, M., Delshad, M., Dwarakanath, V., McKinney, D., Pope, G., Sepehrnoori, K., Tilburg, C., and Jackson, R., Partitioning tracer test for detection, estimation, and remediation performance assessment of subsurface non-aqueous phase liquids. DOI: 10.1029/95WR00174
-
Oliver, D., Cunha, L., and Reynolds, A., Markov chain Monte Carlo methods for conditioning a permeability field to pressure data. DOI: 10.1007/BF02769620
-
Datta-Gupta, A., Yoon, S., Barman, I., and Vasco, D., Streamline-based production-data integration into high-resolution reservoir models.
-
Datta-Gupta, A., Streamline simulation: A technology update. DOI: 10.2118/65604-JPT
-
Wang, Y. and Kovscek, A., Streamline approach for history matching production data. DOI: 10.2118/58350-PA
-
Abbaszadeh-Dehghani, M. and Brigham, W., Analysis of well-to-well tracer flow to determine reservoir layering. DOI: 10.2118/10760-PA
-
Lake, L., Enhanced Oil Recovery.
-
Kass, W., Tracing Technique in Geohydrology.
-
Agca, C., Pope, G., and Sepehrnoori, K., Modelling and analysis of tracer flow in oil reservoirs. DOI: 10.1016/0920-4105(90)90042-2
-
Zemel, B., Tracers in the Oil Field.
-
Shook, G., Ansley, S., and Wyile, A., Tracers and tracer testing: Design, implementation, and interpretation methods. DOI: 10.2172/910642
-
Rieckermann, J., Borsuk, M., Sydler, D., Gujer, W., and Reichert, P., Bayesian experimental design of tracer studies to monitor wastewater leakage from sewer networks. DOI: 10.1029/2009WR008630
-
Lee, H., Higdon, D., Bi, Z., Ferreira, M., and West, M., Markov random field models for high-dimensional parameters in simulations of fluid flow in porous media. DOI: 10.1198/004017002188618419
-
Ma, X., Al-Harbi, M., Datta-Gupta, A., and Efendiev, Y., An efficient two-stage sampling method for uncertainty quantification in history matching geological models. DOI: 10.2118/102476-PA
-
Efendiev, Y., Datta-Gupta, A., Ginting, V., Ma, X., and Mallick, B., An efficient two-stage Markov chain Monte Carlo method for dynamic data integration. DOI: 10.1029/2004WR003764
-
Douglas, C., Efendiev, Y., Ewing, R., Ginting, V., and Lazarov, R., Dynamic data driven simulations in stochastic environments. DOI: 10.1007/s00607-006-0165-3
-
Efendiev, Y., Hou, T., and Luo, W., Preconditioning Markov chain Monte Carlo simulations using coarse-scale models. DOI: 10.1137/050628568
-
Loève, M., Probability theory. DOI: 10.1007/978-1-4684-9464-8
-
Higdon, D., Lee, H., and Bi, Z., A Bayesian approach to characterizing uncertainty in inverse problems using coarse and fine-scale information. DOI: 10.1109/78.978393
-
Fox, C. and Nicholls, G., Mardia, K., Gill, C., and Aykroyd, R. (Eds.), The art and science of Bayesian image analysis.
-
Christen, J. A. and Fox, C., Markov chain Monte Carlo using an approximation. DOI: 10.1198/106186005X76983
-
Ginting, V., Pereira, F., and Rahunanthan, A., Multi-stage Markov chain Monte Carlo methods for porous media flows.
-
Pereira, F. and Rahunanthan, A., Numerical simulation of two-phase flows on a GPU.
-
Chen, Z., Huan, G., and Ma, Y., Computational Methods for Multiphase Flows in Porous Media. DOI: 10.1137/1.9780898718942
-
Douglas Jr., J., Furtado, F., and Pereira, F., On the numerical simulation of waterflooding of heterogeneous petroleum reservoirs. DOI: 10.1023/A:1011565228179
-
Abreu, E., Douglas Jr., J., Furtado, F., and Pereira, F., Operator splitting based on physics for flow in porous media.
-
Abreu, E., Douglas Jr., J., Furtado, F., and Pereira, F., Operator splitting for three-phase flow in heterogeneous porous media. DOI: 10.4208/cicp.2009.v6.p72
-
Aquino, A., Pereira, T., Francisco, A., Pereira, F., and Amaral Souto, H., A Lagrangian strategy for the numerical simulation of radionuclide transport problems. DOI: 10.1016/j.pnucene.2009.06.018
-
Raviart, P. and Thomas, J., A mixed finite element method for 2nd order elliptic problems, Galligani, I. and Magenes, E. (Eds.), Mathematical Aspects of Finite Element Methods. DOI: 10.1007/BFb0064451
-
Liebmann, M., Efficient PDE solvers on modern hardware with applications in medical and technical sciences.
-
Kurganov, A. and Tadmor, E., New high-resolution central schemes for nonlinear conservation laws and convection-diffusion equations. DOI: 10.1006/jcph.2000.6459
-
Pereira, F. and Rahunanthan, A., A semi-discrete central scheme for the approximation of two-phase flows in three space dimensions. DOI: 10.1016/j.matcom.2011.01.012
-
Wong, E., Stochastic Processes in Information and Dynamical Systems.
-
Ginting, V., Pereira, F., Presho, M., and Wo, S., Application of the two-stage Markov chain Monte Carlo method for characterization of fractured reservoirs using a surrogate flow model. DOI: 10.1007/s10596-011-9236-4
-
Dagan, G., Flow and Transport in Porous Formations. DOI: 10.1007/978-3-642-75015-1
-
Frauenfelder, P., Schwab, C., and Todor, R., Finite elements for elliptic problems with stochastic coefficients. DOI: 10.1016/j.cma.2004.04.008
-
Durlofsky, L., Numerical calculation of equivalent grid block permeability tensors for heterogeneous porous media. DOI: 10.1029/91WR00107
-
Gamerman, D. and Lopes, H., Markov Chain Monte Carlo—Stochastic simulation for Bayesian inference.
-
Vasco, D. and Datta-Gupta, A., Asymptotic solutions for solute transport: A formalism for tracer tomography. DOI: 10.1029/98WR02742
-
Nessyahu, N. and Tadmor, E., Non-oscillatory central differencing for hyperbolic conservation laws. DOI: 10.1016/0021-9991(90)90260-8
-
Kurganov, A. and Petrova, G., A third-order semi-discrete genuinely multidimensional central scheme for hyperbolic conservation laws and related problems. DOI: 10.1007/PL00005455
-
Shu, C.-W. and Osher, S., Efficient implementation of essentially nonoscillatory shock-capturing schemes II. DOI: 10.1016/0021-9991(89)90222-2
-
Ginting V., Pereira F., Rahunanthan A., Multi-physics Markov chain Monte Carlo methods for subsurface flows, Mathematics and Computers in Simulation, 118, 2015. Crossref
-
Ginting V., Pereira F., Rahunanthan A., A prefetching technique for prediction of porous media flows, Computational Geosciences, 18, 5, 2014. Crossref
-
Ali Alsadig, Al-Mamun Abdullah, Pereira Felipe, Rahunanthan Arunasalam, Markov Chain Monte Carlo Methods for Fluid Flow Forecasting in the Subsurface, in Computational Science – ICCS 2020, 12143, 2020. Crossref
-
Ali Alsadig, Al-Mamun Abdullah, Pereira Felipe, Rahunanthan Arunasalam, Conditioning by Projection for the Sampling from Prior Gaussian Distributions, in Computational Science and Its Applications – ICCSA 2021, 12952, 2021. Crossref
-
Feldmann R., Gehb C. M., Schaeffner M., Melz T., A Methodology for the Efficient Quantification of Parameter and Model Uncertainty, Journal of Verification, Validation and Uncertainty Quantification, 7, 3, 2022. Crossref
-
Al-Mamun A., Barber J., Ginting V., Pereira F., Rahunanthan A., Contaminant transport forecasting in the subsurface using a Bayesian framework, Applied Mathematics and Computation, 387, 2020. Crossref