Uncertainty Quantification and Management in Model Calibration and History Matching with Ensemble Kalman Methods
- Ali A. Al-Turki (Saudi Aramco) | Ali A. Al-Taiban (Saudi Aramco) | Majdi A. Baddourah (Saudi Aramco) | Babatunde O. Moriwawon (Saudi Aramco) | Zaid A. Sawlan (KAUST)
- Document ID
- Society of Petroleum Engineers
- SPE Europec featured at 82nd EAGE Conference and Exhibition, 8-11 December, Amsterdam, The Netherlands
- Publication Date
- Document Type
- Conference Paper
- 2020. Society of Petroleum Engineers
- 5.5.5 Evaluation of uncertainties, 5.4.1 Waterflooding, 5.5.8 History Matching, 7.6.6 Artificial Intelligence, 7.6 Information Management and Systems, 5.1.2 Faults and Fracture Characterisation, 5.5 Reservoir Simulation, 7 Management and Information, 5 Reservoir Desciption & Dynamics, 5.4 Improved and Enhanced Recovery
- Ensemble Kalman Filter, Ensemble Kalman Smoother, Stochastic, Optimization, Assisted History Matching
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History matching field performance is a time-consuming, complex and non-unique inverse problem that yields multiple plausible solutions. This is due to the inherent uncertainty associated with geological and flow modeling. The history matching must be performed diligently with the ultimate objective of availing reliable prediction tools for managing the oil and gas assets. Our work capitalizes on the latest development in ensemble Kalman techniques, namely, the Ensemble Kalman Filter and Smoother (EnKF/S) to properly quantify and manage reservoir models’ uncertainty throughout the process of model calibration and history matching.
Sequential and iterative EnKF/S algorithms have been developed to overcome the shortcomings of the existing methods such as the lack of data assimilation capabilities and abilities to quantify and manage uncertainties, in addition to the huge number of simulations runs required to complete a study. An initial ensemble of 40 to 50 equally probable reservoir models was generated with variable areal, vertical permeability and porosity. The initial ensemble captured the most influencing reservoir properties, which will be propagated and honored by the subsequent ensemble iterations. Data misfits between the field historical data and simulation data are calculated for each of the realizations of reservoir models to quantify the impact of reservoir uncertainty, and to perform the necessary changes on horizontal, vertical permeability and porosity values for the next iteration. Each generation of the optimization process reduces the data misfit compared to the previous iteration. The process continues until a satisfactory field level and well level history match is reached or when there is no more improvement.
In this study, an application of EnKF/S is demonstrated for history matching of a faulted reservoir model under waterflooding conditions. The different implementations of EnKF/S were compared. EnKF/S preserved key geological features of the reservoir model throughout the history matching process. During this study, EnKF/S served as a bridge between classical control theory solutions and Bayesian probabilistic solutions of sequential inverse problems. EnKF/S methods demonstrated good tracking qualities while giving some estimate of uncertainty as well.
The updated reservoir properties (horizontal, vertical permeability and porosity values) are conditioned throughout the EnKF/S processes (cycles), maintaining consistency with the initial geological understanding. The workflow resulted in enhanced history match quality in shorter turnaround time with much fewer simulation runs than the traditional genetic or Evolutionary algorithms. The geological realism of the model is retained for robust prediction and development planning.
|File Size||1 MB||Number of Pages||13|