Fast Model Update Coupled to an Ensemble based Closed Loop Reservoir Management

Skjervheim, J. A.; Hanea, R.G.; Evensen, Geir

doi:10.3997/2214-4609.201413629

Cited by 10 publications

(4 citation statements)

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“…In EnKF it is required that all realizations have the same grid and active grid cells, and mainly petrophysical parameters such as gridcell porosity and permeability, and scalar parameters such as fault transmissibilities, are updated in addition to the state variables. When using ensemble smoothers, it is possible to update so-called hyperparameters such as channel facies directions, sub-seismic fault density, and even structural parameters including the grid itself [16,27,29]. Hyperparameters are parameters that are input to the geological modeling process in contrast to the parameters that are output from the geological modeling and which we usually update in the conditioning.…”

Section: Introductionmentioning

confidence: 99%

Conditioning reservoir models on rate data using ensemble smoothers

Evensen¹,

Eikrem

2018

Comput Geosci

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There are several issues to consider when we use ensemble smoothers to condition reservoir models on rate data. The values in a time series of rate data contain redundant information that may lead to poorly conditioned inversions and thereby influence the stability of the numerical computation of the update. A time series of rate data typically has correlated measurement errors in time, and negligence of the correlations leads to a too strong impact from conditioning on the rate data and possible ensemble collapse. The total number of rate data included in the smoother update will typically exceed the ensemble size, and special care needs to be taken to ensure numerically stable results. We force the reservoir model with production rate data derived from the observed production, and the further conditioning on the same rate data implies that we use the data twice. This paper discusses strategies for conditioning reservoir models on rate data using ensemble smoothers. In particular, a significant redundancy in the rate data makes it possible to subsample the rate data. The alternative to subsampling is to model the unknown measurement error correlations and specify the full measurement error covariance matrix. We demonstrate the proposed strategies using different ensemble smoothers with the Norne full-field reservoir model.

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Section: Introductionmentioning

confidence: 99%

Conditioning reservoir models on rate data using ensemble smoothers

Evensen¹,

Eikrem

2018

Comput Geosci

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“…The update loop used in this paper is compatible with a number of ensemblebased methods which have previously been implemented for reservoir data as-similation including the ensemble Kalman filter (Aanonsen et al, 2009), ensemble smoother (Skjervheim and Evensen, 2011;Skjervheim et al, 2015), the particle filter (Lorentzen et al, 2016), and more sophisticated combinations of the above, such as adaptive Gaussian mixture filter (Lorentzen et al, 2017). To demonstrate the workflow, we use the standard ensemble Kalman filter (EnKF) method (Chen et al, 2015;Luo et al, 2015) for the implementation described in this paper.…”

Section: Ensemble-based Update Algorithmmentioning

confidence: 99%

“…The trajectory optimization in the DSS is inspired by the discretized stochastic dynamic programming algorithms for geosteering that were discussed in Kullawan et al (2017Kullawan et al ( , 2018. However, the DSS presented here is specifically optimized for usage with ensemble-based update workflows which are already used for field development planning (Hanea et al, 2015;Skjervheim et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A decision support system for multi-target geosteering

Alyaev

Suter

Bratvold

et al. 2019

Journal of Petroleum Science and Engineering

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Geosteering is a sequential decision process under uncertainty. The goal of geosteering is to maximize the expected value of the well, which should be defined by an objective value-function for each operation.In this paper we present a real-time decision support system (DSS) for geosteering that aims to approximate the uncertainty in the geological interpretation with an ensemble of geomodel realizations. As the drilling operation progresses, the ensemble Kalman filter is used to sequentially update the realizations using the measurements from real-time logging while drilling. At every decision point a discrete dynamic programming algorithm computes all potential well trajectories for the entire drilling operation and the corresponding value of the well for each realization. Then, the DSS considers all immediate alternatives (continue/steer/stop) and chooses the one that gives the best predicted value across the realizations. This approach works for a variety of objectives and constraints and suggests reproducible decisions under uncertainty. Moreover, it has real-time performance.The system is tested on synthetic cases in a layer-cake geological environment where the target layer should be selected dynamically based on the prior (predrill) model and the electromagnetic observations received while drilling. The numerical closed-loop simulation experiments demonstrate the ability of the DSS to perform successful geosteering and landing of a well for different geological configurations of drilling targets. Furthermore, the DSS allows to adjust and reweight the objectives, making the DSS useful before fully-automated geosteering becomes reality.

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“…In a reservoir history-matching problem, the model operator is typically the reservoir simulation model, which, given the input forcing and a parameterization, which defines the model, simulates the produced fluid rates. Some recent applications use a full Fast-Model-Update workflow [7,8] where the model workflow can also contain, e.g., a geological forward model, a reservoir simulation model, and a seismic forward model, which, given the inputs to the geological description, predicts the production of oil, water, gas, and the seismic response from the reservoir. Given a set of input parameters, x, the prediction of y is precisely determined by the model in Equation (1).…”

Section: History-matching Problemmentioning

confidence: 99%

Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching

Evensen

Raanes

Stordal

et al. 2019

Front. Appl. Math. Stat.

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Raanes et al. [1] revised the iterative ensemble smoother of Chen and Oliver [2, 3], denoted Ensemble Randomized Maximum Likelihood (EnRML), using the property that the EnRML solution is contained in the ensemble subspace. They analyzed EnRML and demonstrated how to implement the method without the use of expensive pseudo inversions of the low-rank state covariance matrix or the ensemble-anomaly matrix. The new algorithm produces the same result, realization by realization, as the original EnRML method. However, the new formulation is simpler to implement, numerically stable, and computationally more efficient. The purpose of this document is to present a simple derivation of the new algorithm and demonstrate its practical implementation and use for reservoir history matching. An additional focus is to customize the algorithm to be suitable for big-data assimilation of measurements with correlated errors. The computational cost of the resulting "ensemble sub-space" algorithm is linear in both the dimension of the state space and the number of measurements, also when the measurements have correlated errors. The final algorithm is implemented in the Ensemble Reservoir Tool (ERT) for running and conditioning ensembles of reservoir models. Several verification experiments are presented.

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Fast Model Update Coupled to an Ensemble based Closed Loop Reservoir Management

Cited by 10 publications

References 0 publications

Conditioning reservoir models on rate data using ensemble smoothers

Conditioning reservoir models on rate data using ensemble smoothers

A decision support system for multi-target geosteering

Efficient Implementation of an Iterative Ensemble Smoother for Data Assimilation and Reservoir History Matching

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