Probabilistic Collocation Based Kalman Filter for Assisted History Matching - A Case Study

2011 ◽  
Author(s):  
Weixuan Li ◽  
Adedayo Oyerinde ◽  
David Stern ◽  
Xiao-hui Wu ◽  
Dongxiao Zhang
Keyword(s):  
Kalman Filter ◽  
History Matching ◽  
Assisted History Matching ◽  
Probabilistic Collocation

Software Assisted History Matching - A Case Study of a Very Constrained Problem with Intensive Pressure and Saturation Data.

2006 ◽  
Author(s):  
Shawket G. Ghedan ◽  
Adrian P. Gibson ◽  
Ilhan Sener ◽  
Ozgur Eylem Gunal ◽  
Alexander Diab ◽  
...  
Keyword(s):  
History Matching ◽  
Constrained Problem ◽  
Assisted History Matching

A Probabilistic Collocation-Based Kalman Filter for History Matching

SPE Journal ◽  
2011 ◽  
Vol 16 (02) ◽  
pp. 294-306 ◽  
Author(s):  
Lingzao Zeng ◽  
Haibin Chang ◽  
Dongxiao Zhang
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
History Matching ◽  
Initial Conditions ◽  
Collocation Point ◽  
Computational Effort ◽  
Production Data ◽  
Sampling Errors ◽  
Probabilistic Collocation

Summary The ensemble Kalman filter (EnKF) has been used widely for data assimilation. Because the EnKF is a Monte Carlo-based method, a large ensemble size is required to reduce the sampling errors. In this study, a probabilistic collocation-based Kalman filter (PCKF) is developed to adjust the reservoir parameters to honor the production data. It combines the advantages of the EnKF for dynamic data assimilation and the polynomial chaos expansion (PCE) for efficient uncertainty quantification. In this approach, all the system parameters and states and the production data are approximated by the PCE. The PCE coefficients are solved with the probabilistic collocation method (PCM). Collocation realizations are constructed by choosing collocation point sets in the random space. The simulation for each collocation realization is solved forward in time independently by means of an existing deterministic solver, as in the EnKF method. In the analysis step, the needed covariance is approximated by the PCE coefficients. In this study, a square-root filter is employed to update the PCE coefficients. After the analysis, new collocation realizations are constructed. With the parameter collocation realizations as the inputs and the state collocation realizations as initial conditions, respectively, the simulations are forwarded to the next analysis step. Synthetic 2D water/oil examples are used to demonstrate the applicability of the PCKF in history matching. The results are compared with those from the EnKF on the basis of the same analysis. It is shown that the estimations provided by the PCKF are comparable to those obtained from the EnKF. The biggest improvement of the PCKF comes from the leading PCE approximation, with which the computational burden of the PCKF can be greatly reduced by means of a smaller number of simulation runs, and the PCKF outperforms the EnKF for a similar computational effort. When the correlation ratio is much smaller, the PCKF still provides estimations with a better accuracy for a small computational effort.

Adjoint-Based Assisted History Matching: A Niger Delta Field Case Study

2017 ◽  
Author(s):  
Chukwuma Onwuchekwa ◽  
Linda Dennar ◽  
Suleiman Ahmed ◽  
Olatunji Bakare ◽  
Chima Emelle
Keyword(s):  
Niger Delta ◽  
History Matching ◽  
Field Case ◽  
Assisted History Matching
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