History Matching Using the Ensemble Kalman Filter on a North Sea Field Case

Haugen, Vibeke Eilen Jensen; Natvik, Lars-Jorgen; Evensen, Geir; Berg, Aina; Flornes, Kristin M.; Nævdal, Gunnar

doi:10.2118/102430-ms

Cited by 35 publications

(6 citation statements)

References 22 publications

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“…Hendricks Franssen and Kinzelbach [] explored the performance of EnKF in the presence of uncertain recharge rate and transmissivity; analyzed the filter inbreeding problem, which causes EnKF to increasingly underestimate parameter variance with time; and suggested that the problem could be partially remedied by (1) a damping parameter that limits perturbation of the parameters in each representation, (2) a correction term applied to the parameter covariance matrix, and (3) starting EnKF with a large number of representations, then retaining only those with relatively high probabilities following the first simulation period. EnKF has been shown to outperform traditional batch history matching in the quality of porosity and permeability estimates of petroleum reservoirs [ Haugen et al ., ]. Li et al .…”

Section: Introductionmentioning

confidence: 99%

Data assimilation and parameter estimation via ensemble Kalman filter coupled with stochastic moment equations of transient groundwater flow

Panzeri

Riva

Guadagnini

et al. 2013

Water Resources Research

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[1] The ensemble Kalman filter (EnKF) is a powerful tool for assimilating data in earth system models. The approach allows real time Bayesian updating of system states and parameters as new data become available. This paper focuses on EnKF data assimilation in models of groundwater flow through complex geologic media. It has become common to treat the hydraulic conductivity of such media as correlated random fields conditioned on measured conductivity (medium property) and/or hydraulic head (system state) values. This renders the conductivity nonstationary and the corresponding conditional flow equations stochastic. Solving these equations and coupling them with EnKF generally entails computationally intensive Monte Carlo (MC) simulation. We propose to circumvent the need for MC through a direct solution of approximate nonlocal (integrodifferential) equations that govern the space-time evolution of conditional ensemble means (statistical expectations) and covariances of hydraulic heads and fluxes. We illustrate and explore our approach on synthetic two-dimensional examples in which a well pumps water from a randomly heterogeneous aquifer subject to prescribed head and flux boundary conditions. Embedding the solution in EnKF provides sequential updates of conductivity and head estimates throughout the space-time domain of interest. We demonstrate the computational feasibility and accuracy of our methodology, showing that hydraulic conductivity estimates are more sensitive to early than to later head values and improve with increasing assimilation frequency at early time.

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Section: Introductionmentioning

confidence: 99%

Data assimilation and parameter estimation via ensemble Kalman filter coupled with stochastic moment equations of transient groundwater flow

Panzeri

Riva

Guadagnini

et al. 2013

Water Resources Research

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“…During the last decade application of a number of global optimization methods have gained popularity in the automated history matching process. Among the successful methods we can include the ensemble Kalman filter (Van Leeuwen 1999, Evensen 2003, Haugen, et al 2006, Aanonsen, et al 2009, Hanea, et al 2010, Szklarz, Hanea and Peters 2011, Neighborhood Algorithm (Christie, MacBeth and Subbey 2002, Stephen, et al 2006, Rotondi, et al 2006, Subbey and Christie 2003, Genetic Algorithms (Erbas and Christie 2007) (Castellini 2005), Scatter search (Sousa 2007), Tabu Search (Yang, Ngheim and Card 2007) Carlo (Maucec 2007), and Chaotic Optimization (Mantica 2002). Rwechungura et al (2011) have published fascinating information regarding the increasing interest in history matching since 1990.…”

Section: A Literature Review On History Matchingmentioning

confidence: 99%

Assisted history matching using pattern recognition technology

Shahkarami¹,

Mohaghegh²,

Hajizadeh³

2018

IJOGCT

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Assisted History Matching Using Pattern Recognition Technology Alireza Shahkarami Reservoir simulation and modeling is utilized throughout field development in different capacities. Sensitivity analysis, history matching, operations optimization and uncertainty assessment are the conventional analyses in full field model studies. Realistic modeling of the complexities of a reservoir requires a large number of grid blocks. As the complexity of a reservoir increases and consequently the number of grid blocks, so does the time required to accomplish the abovementioned tasks.

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“…Although the quantitative use of timelapse seismic data for history matching still remains a challenging task, significant work has been conducted to overcome this difficulty (Skjervheim et al, 2007;Feng and Mannseth, 2010;Trani et al, 2013;Luo et al, 2017;Zhang and Leeuwenburgh, 2017;. Meanwhile, a growing number of real-field history matching applications using ensemble-based assimilation methods have been presented showing satisfactory results in the literature (Haugen et al, 2008;Zhang and Oliver, 2011;Chen and Oliver, 2014;Abadpour et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Efficient Assimilation of Crosswell Electromagnetic Data Using Ensemble-Based History-Matching Framework

Zhang

Hoteit

2019

SPE Reservoir Simulation Conference

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This template is provided to give authors a basic shell for preparing your manuscript for submittal to an SPE meeting or event. Styles have been included (Head1, Head2, Para, FigCaption, etc.) to give you an idea of how your finalized paper will look before it is published by SPE. All manuscripts submitted to SPE will be extracted from this template and tagged into an XML format; SPE's standardized styles and fonts will be used when laying out the final manuscript. Links will be added to your manuscript for references, tables, and equations. Figures and tables should be placed directly after the first paragraph they are mentioned in. The technical content of your paper WILL NOT be changed. Please start your manuscript below.

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History Matching Using the Ensemble Kalman Filter on a North Sea Field Case

Cited by 35 publications

References 22 publications

Data assimilation and parameter estimation via ensemble Kalman filter coupled with stochastic moment equations of transient groundwater flow

Data assimilation and parameter estimation via ensemble Kalman filter coupled with stochastic moment equations of transient groundwater flow

Assisted history matching using pattern recognition technology

Efficient Assimilation of Crosswell Electromagnetic Data Using Ensemble-Based History-Matching Framework

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