Adsorption/Desorption of Tungsten from Ammonium Tungstate Pentahydrate using Anion Exchange Resins

doi:10.12972/ksmer.2014.51.3.437

All Issue

2014 Vol.51, Issue 3 Preview Page Next Page

Research Paper

Adsorption/Desorption of Tungsten from Ammonium Tungstate Pentahydrate using Anion Exchange Resins 유선기반 역산과 생산량기반 거리를 이용한 저류층거동의 불확실성 평가: 박지훈·오창훈·진정우·최종근
Jonghyuk Jeon, Younghun Kim, Jinyoung Lee, Choon Han

30 June 2014. pp. 437-447

PDF

Abstract

Reservoir characterization and uncertainty assessment are indispensable for decision making. Uncertainty quantification requires high computational costs because multiple models are simulated and optimizations should be performed on each model. In addition, fluid flow in a reservoir exhibits highly nonlinear characteristics, and history matching is mathematically an ill-posed problem. In this study, we propose a methodology to rapidly quantify uncertainties by using flow based distance and generalized travel time inversion(GTTI). Firstly, we group similar models according to cumulative field water production. Representative models are selected from each group after applying K-means clustering. Then inversions are performed on the representative models to quantify uncertainties. We use GTTI algorithm to take advantage of computational efficiency and quasilinearity. Compared to a conventional method, the proposed method reduces the amount of calculations significantly, while reliably assessing the uncertainty.

Keywords

Uncertainty quantification

Generalized travel time inversion(GTTI)

Flow based distance

Reservoir characterization

저류층특성화와 불확실성 정량화는 의사결정을 위한 필수과정이다. 불확실성 정량화는 다수의 모델을 계산하며 각 모델의 최적화가 요구되기 때문에 많은 계산이 필요하다. 또한 저류층유체의 유동은 매우 비선형적이며 히스토리매칭은 수학적으로 잘 정립되지 못한 문제이다. 본 연구에서는 생산량기반 거리와 generalized travel time inversion(GTTI)을 이용하여 빠르게 불확실성을 정량화하는 방법을 제시하였다. 먼저 필드 누적물생산량에 따라 비슷한 필드를 군집화하였다. 이후 K-평균 군집화를 실시하여 대표모델을 선택하고 대표모델에 대해 역산을 실시하였다. 역산 시 GTTI 알고리즘을 적용하여 계산효율 및 준선형성의 이점을 활용하였다. 제시된 방법은 기존의 방법에 비해 불확실성을 신뢰성 있게 정량화하면서도 계산량을 획기적으로 줄인 것을 확인하였다.

키워드

불확실성 정량화

Generalized travel time inversion(GTTI)

생산량기반 거리

저류층특성화

References

Cheng, H., Wen, X., Milliken, W. and Datta-Gupta, A., 2004, “Field Experiences with Assisted and Automatic History Matching Using Streamline Models,” Paper SPE 89857 presented at Proc. of the SPE Annual Technical Conference and Exhibition, Houston, Texas, USA, Sept. 26-29.
Cheng, H., Kharghoria, A. and Datta-Gupta, A., 2005, “Fast History Matching of Finite-difference Models Using Streamline-derived Sensitivities,” SPE Reservoir Evaluation & Engineering, Vol. 8, No. 5, pp. 426-436.
Choe, J., 2007, Geostatistics, SigmaPress, Seoul, Korea.
Datta-Gupta, A., 2000, “Streamline Simulation: a Technology Update,” J. of Petroleum Technology, Vol. 52, No. 12, pp. 68-73.
Gao, G., Zafari, M. and Reynolds, A.C., 2005, “Quantifying Uncertainty for the PUNQ-S3 Problem in a Bayesian Setting with RML and EnKF,” Paper SPE 93324 presented at Proc. of the SPE Reservoir Simulation Symposium, Houston, Texas, USA, Jan. 31-Feb. 2.
He, Z., Yoon, S. and Datta-Gupta, A., 2002, “Streamline-based Production Data Integration with Gravity and Changing Field Conditions,” SPE J., Vol. 7, No. 4, pp. 423-436.
Jang, M. and Choe, J., 2001, “Modeling of Contaminant Transport in a Fracture Using Streamline Simulation,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 38, No. 3, pp. 202-210.
Jin, J., Lim, J., Lee, H. and Choe, J., 2011, “Metric Space Mapping of Oil Sands Reservoirs Using Streamline Simulation,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 14, No. 3, pp. 109-113.
Jung, S. and Choe, J., 2009, “Improvement of Numerical Stability in History-matching Using Streamline Assisted Ensemble Kalman Filter,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 46, No. 4, pp. 453-463.
Ki, S., Jeong, H. and Choe, J., 2009, “Inverse Modeling Using Probabilistic Sensitivity Analysis,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 46, No. 1, pp. 1-10.
Kitanidis, P.K., 1995, “Quasilinear Geostatistical Theory for Inversing,” Water Resources Research, Vol. 31, No. 10, pp. 2411-2419.
Oliver, D.S., Reynolds, A.C. and Liu, N., 2008, Inverse Theory for Petroleum Reservoir Characterization and History Matching, Cambridge University Press, Cambridge, UK.
Park, K. and Choe, J., 2004, “Optimizing the Distribution of Monitoring Wells for Aquifer Characterization,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 41, No. 5, pp. 373-380.
Park, K., Shin, Y., and Choe, J., 2006, “Real-time Reservoir Characterization Using Ensemble Kalman Filter During Waterflooding,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 43, No. 2, pp. 143-150.
Pollock, D.W., 1988, “Semianalytical Computation of Path Lines for Finite-difference Models,” Ground Water, Vol. 26, No. 6, pp. 743-750.
Scheidt, C. and Caers, J., 2009. “Representing Spatial Uncertainty Using Distances and Kernels,” Mathematical Geosciences, Vol. 41, No. 4, pp. 397-419.
Thiele, M.R., 2005, “Streamline Simulation,” Proc. of the 8th International Forum on Reservoir Simulation, Lago Maggiore, Italy, Jun. 20-24, pp. 1-47.
Vasco, D.W., Yoon, S. and Datta-Gupta, A., 1999, “Integrating Dynamic Data into High-resolution Reservoir Models Using Streamline-based Analytic Sensitivity Coefficients,” SPE J., Vol. 4, No. 4, pp. 389-399.
Wu, Z. and Datta-Gupta, A., 2001, “Rapid History Matching Using a Generalized Travel Time Inversion Method,” Paper SPE 66352 presented at Proc. of the SPE Reservoir Simulation Symposium, Houston, Texas, USA, Feb. 11-14.

Information

Publisher :The Korean Society of Mineral and Energy Resources Engineers
Publisher(Ko) :한국자원공학회
Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
Journal Title(Ko) :한국자원공학회지
Volume : 51
No :3
Pages :437-447
DOI :https://doi.org/10.12972/ksmer.2014.51.3.437

[1] Cheng, H., Wen, X., Milliken, W. and Datta-Gupta, A., 2004, “Field Experiences with Assisted and Automatic History Matching Using Streamline Models,” Paper SPE 89857 presented at Proc. of the SPE Annual Technical Conference and Exhibition, Houston, Texas, USA, Sept. 26-29.

[2] Cheng, H., Kharghoria, A. and Datta-Gupta, A., 2005, “Fast History Matching of Finite-difference Models Using Streamline-derived Sensitivities,” SPE Reservoir Evaluation & Engineering, Vol. 8, No. 5, pp. 426-436.

[3] Choe, J., 2007, Geostatistics, SigmaPress, Seoul, Korea.

[4] Datta-Gupta, A., 2000, “Streamline Simulation: a Technology Update,” J. of Petroleum Technology, Vol. 52, No. 12, pp. 68-73.

[5] Gao, G., Zafari, M. and Reynolds, A.C., 2005, “Quantifying Uncertainty for the PUNQ-S3 Problem in a Bayesian Setting with RML and EnKF,” Paper SPE 93324 presented at Proc. of the SPE Reservoir Simulation Symposium, Houston, Texas, USA, Jan. 31-Feb. 2.

[6] He, Z., Yoon, S. and Datta-Gupta, A., 2002, “Streamline-based Production Data Integration with Gravity and Changing Field Conditions,” SPE J., Vol. 7, No. 4, pp. 423-436.

[7] Jang, M. and Choe, J., 2001, “Modeling of Contaminant Transport in a Fracture Using Streamline Simulation,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 38, No. 3, pp. 202-210.

[8] Jin, J., Lim, J., Lee, H. and Choe, J., 2011, “Metric Space Mapping of Oil Sands Reservoirs Using Streamline Simulation,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 14, No. 3, pp. 109-113.

[9] Jung, S. and Choe, J., 2009, “Improvement of Numerical Stability in History-matching Using Streamline Assisted Ensemble Kalman Filter,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 46, No. 4, pp. 453-463.

[10] Ki, S., Jeong, H. and Choe, J., 2009, “Inverse Modeling Using Probabilistic Sensitivity Analysis,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 46, No. 1, pp. 1-10.

[11] Kitanidis, P.K., 1995, “Quasilinear Geostatistical Theory for Inversing,” Water Resources Research, Vol. 31, No. 10, pp. 2411-2419.

[12] Oliver, D.S., Reynolds, A.C. and Liu, N., 2008, Inverse Theory for Petroleum Reservoir Characterization and History Matching, Cambridge University Press, Cambridge, UK.

[13] Park, K. and Choe, J., 2004, “Optimizing the Distribution of Monitoring Wells for Aquifer Characterization,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 41, No. 5, pp. 373-380.

[14] Park, K., Shin, Y., and Choe, J., 2006, “Real-time Reservoir Characterization Using Ensemble Kalman Filter During Waterflooding,” J. of the Korean Society of Mineral and Energy Resources Engineers, Vol. 43, No. 2, pp. 143-150.

[15] Pollock, D.W., 1988, “Semianalytical Computation of Path Lines for Finite-difference Models,” Ground Water, Vol. 26, No. 6, pp. 743-750.

[16] Scheidt, C. and Caers, J., 2009. “Representing Spatial Uncertainty Using Distances and Kernels,” Mathematical Geosciences, Vol. 41, No. 4, pp. 397-419.

[17] Thiele, M.R., 2005, “Streamline Simulation,” Proc. of the 8th International Forum on Reservoir Simulation, Lago Maggiore, Italy, Jun. 20-24, pp. 1-47.

[18] Vasco, D.W., Yoon, S. and Datta-Gupta, A., 1999, “Integrating Dynamic Data into High-resolution Reservoir Models Using Streamline-based Analytic Sensitivity Coefficients,” SPE J., Vol. 4, No. 4, pp. 389-399.

[19] Wu, Z. and Datta-Gupta, A., 2001, “Rapid History Matching Using a Generalized Travel Time Inversion Method,” Paper SPE 66352 presented at Proc. of the SPE Reservoir Simulation Symposium, Houston, Texas, USA, Feb. 11-14.

Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

All Issue