Ensemble Based 4D Seismic History Matching: Integration of Different Levels and Types of Seismic Data

Fahimuddin, Abul; Aanonsen, Sigurd Ivar; Skjervheim, Jan-Arild

doi:10.2118/131453-ms

Cited by 30 publications

(4 citation statements)

References 13 publications

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“…There may be more to gain by further exploring more advanced adaptive inflation methods, as well as localization methods based on streamline simulation, both of which have not been considered in this study. Finally, Fahimuddin et al [13] have compared the use of amplitude and impedance data and found the latter to produce somewhat better results. More research is needed, however, in order to fully understand the observed differences as well as potential benefits of specific types of seismic attributes.…”

Section: Discussionmentioning

confidence: 99%

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Ensemble-based conditioning of reservoir models to seismic data

2010

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While 3D seismic has been the basis for geological model building for a long time, time-lapse seismic has primarily been used in a qualitative manner to assist in monitoring reservoir behavior. With the growing acceptance of assisted history matching methods has come an equally rising interest in incorporating 3D or time-lapse seismic data into the history matching process in a more quantitative manner. The common approach in recent studies has been to invert the seismic data to elastic or to dynamic reservoir properties, typically acoustic impedance or saturation changes. Here we consider the use of both 3D and time-lapse seismic amplitude data based on a forward modeling approach that does not require any inversion in the traditional sense. Advantages of such an approach may be better estimation and treatment of model and measurement errors, the combination of two inversion steps into one by removing the explicit inversion to state space variables, and more consistent dependence on the validity of assumptions underlying the inversion process. In this paper, we introduce this approach with the use of an assisted history matching method in mind. Two ensemble-based methods, the ensemble Kalman filter and the ensemble randomized maximum likelihood method, are used to investigate issues arising from the use of seismic amplitude data, and possible solutions are presented. Experiments with a 3D synthetic reservoir model show that additional information on the distribution of reservoir fluids, and on rock properties such as porosity and permeability, can be extracted from the seismic data. The role for localization and iterative methods are discussed in detail.

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Section: Discussionmentioning

confidence: 99%

“…Haverl et al [20] interpreted the GOC depth from single-survey waveform data and assimilated this depth with the EnKF. Most recently, Fahimuddin et al [13] compared the use of amplitude and impedance data with the EnKF on a sector model based on a North Sea reservoir.…”

Section: Introductionmentioning

confidence: 99%

Ensemble-based conditioning of reservoir models to seismic data

2010

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“…However, the EnKF is computationally efficient only for modest sample size (∼100), which inevitably introduces large sampling errors that requires additional tuning [ Anderson , ] or good sampling strategies [ Evensen , ]. The sampling bias in covariance matrices becomes significant when the number of observations is larger than the ensemble size [ Kepert , ], which is common for assimilating geophysical data [ Fahimuddin et al ., ]. Although remedies like subspace pseudo‐inversion algorithm [ Evensen , ] or sequential version of EnKF [ Houtekamer and Mitchell , ] can reduce the sampling bias, the implementation of EnKF in such cases becomes less efficient.…”

Section: Introductionmentioning

confidence: 99%

A Kalman filter powered by H2-matrices for quasi-continuous data assimilation problems

Ambikasaran

Darve

et al. 2014

Water Resour. Res.

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Continuously tracking the movement of a fluid or a plume in the subsurface is a challenge that is often encountered in applications, such as tracking a plume of injected CO 2 or of a hazardous substance. Advances in monitoring techniques have made it possible to collect measurements at a high frequency while the plume moves, which has the potential advantage of providing continuous high-resolution images of fluid flow with the aid of data processing. However, the applicability of this approach is limited by the high computational cost associated with having to analyze large data sets within the time constraints imposed by real-time monitoring. Existing data assimilation methods have computational requirements that increase superlinearly with the size of the unknowns m. In this paper, we present the HiKF, a new Kalman filter (KF) variant powered by the hierarchical matrix approach that dramatically reduces the computational and storage cost of the standard KF from Oðm 2 Þ to OðmÞ, while producing practically the same results. The version of HiKF that is presented here takes advantage of the so-called random walk dynamical model, which is tailored to a class of data assimilation problems in which measurements are collected quasi-continuously. The proposed method has been applied to a realistic CO 2 injection model and compared with the ensemble Kalman filter (EnKF). Numerical results show that HiKF can provide estimates that are more accurate than EnKF and also demonstrate the usefulness of modeling the system dynamics as a random walk in this context.

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“…Application of the EnKF to seismic history matching has been studied in both synthetic and real field settings (e.g., Skjervheim et al 2007;Haverl et al 2005;Fahimuddin et al 2010;Leeuwenburgh et al 2011). Although several successes could be reported, different authors have noted that incorporation of very large numbers of data may cause gradual loss of rank in the ensemble, leading to unreliable uncertainty estimates and eventually to filter divergence.…”

Section: Introductionmentioning

confidence: 99%

Seismic History Matching of Fluid Fronts Using the Ensemble Kalman Filter

Trani

Arts²,

Leeuwenburgh³

2012

SPE Journal

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Time-lapse seismic data provide information on the dynamics of multiphase reservoir fluid flow in places where no production data from wells are available. This information, in principle, could be used to estimate unknown reservoir properties. However, the amount, resolution, and character of the data have long posed significant challenges for quantitative use in assisted-historymatching workflows. Previous studies, therefore, have generally investigated methods for updating single models with reduced parameter-uncertainty space. Recent developments in ensemblebased history-matching methods have shown the feasibility of multimodel history and matching of production data while maintaining a full uncertainty description. Here, we introduce a robust and flexible reparameterization for interpreted fluid fronts or seismic attribute isolines that extends these developments to seismic history matching. The seismic data set is reparameterized, in terms of arrival times, at observed front positions, thereby significantly reducing the number of data while retaining essential information. A simple 1D example is used to introduce the concepts of the approach. A synthetic 3D example, with spatial complexity that is typical for many waterfloods, is examined in detail. History-matching cases based on both separate and combined use of production and seismic data are examined. It is shown that consistent multimodel history matches can be obtained without the need for reduction of the parameter space or for localization of the impact of observations. The quality of forecasts based on the history-matched models is evaluated by simulating both expected production and saturation changes throughout the field for a fixed operating strategy. It is shown that bias and uncertainty in the forecasts of production both at existing wells and in the flooded area are reduced considerably when both production and seismic data are incorporated. The proposed workflow, therefore, enables better decisions on field developments that require optimal placement of infill wells.

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Ensemble Based 4D Seismic History Matching: Integration of Different Levels and Types of Seismic Data

Cited by 30 publications

References 13 publications

Ensemble-based conditioning of reservoir models to seismic data

Ensemble-based conditioning of reservoir models to seismic data

A Kalman filter powered by H2-matrices for quasi-continuous data assimilation problems

Seismic History Matching of Fluid Fronts Using the Ensemble Kalman Filter

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