A multiresolution adjoint sensitivity analysis of time-lapse saturation maps

Awotunde, Abeeb A.

doi:10.1007/s10596-014-9414-2

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Sparse representation (either wavelet-based or not) is also used for other purposes in petroleum engineering. For instance, it is adopted in [ 57 – 64 ] for re-parametrization of reservoir models, rather than size reduction of observational data. [ 57 ] also used wavelet-based sparse representation to represent time-lapse saturation maps.…”

Section: The Proposed Frameworkmentioning

confidence: 99%

“…For instance, it is adopted in [ 57 – 64 ] for re-parametrization of reservoir models, rather than size reduction of observational data. [ 57 ] also used wavelet-based sparse representation to represent time-lapse saturation maps. However, [ 57 ] does not consider the estimation of noise STD of wavelet coefficients, which is an issue to be addressed below.…”

Section: The Proposed Frameworkmentioning

confidence: 99%

“…[ 57 ] also used wavelet-based sparse representation to represent time-lapse saturation maps. However, [ 57 ] does not consider the estimation of noise STD of wavelet coefficients, which is an issue to be addressed below.…”

Section: The Proposed Frameworkmentioning

confidence: 99%

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Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering

et al. 2018

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Data assimilation is an important discipline in geosciences that aims to combine the information contents from both prior geophysical models and observational data (observations) to obtain improved model estimates. Ensemble-based methods are among the state-of-the-art assimilation algorithms in the data assimilation community. When applying ensemble-based methods to assimilate big geophysical data, substantial computational resources are needed in order to compute and/or store certain quantities (e.g., the Kalman-gain-type matrix), given both big model and data sizes. In addition, uncertainty quantification of observational data, e.g., in terms of estimating the observation error covariance matrix, also becomes computationally challenging, if not infeasible. To tackle the aforementioned challenges in the presence of big data, in a previous study, the authors proposed a wavelet-based sparse representation procedure for 2D seismic data assimilation problems (also known as history matching problems in petroleum engineering). In the current study, we extend the sparse representation procedure to 3D problems, as this is an important step towards real field case studies. To demonstrate the efficiency of the extended sparse representation procedure, we apply an ensemble-based seismic history matching framework with the extended sparse representation procedure to a 3D benchmark case, the Brugge field. In this benchmark case study, the total number of seismic data is in the order of . We show that the wavelet-based sparse representation procedure is extremely efficient in reducing the size of seismic data, while preserving the salient features of seismic data. Moreover, even with a substantial data-size reduction through sparse representation, the ensemble-based seismic history matching framework can still achieve good estimation accuracy.

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Section: The Proposed Frameworkmentioning

confidence: 99%

Section: The Proposed Frameworkmentioning

confidence: 99%

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Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering

et al. 2018

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“…However, it seems that Awotunde (2014) does not consider how to estimate the variance of noise of the transformed data (i.e., wavelet coefficients), which is an issue to be addressed later.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble 4D-Seismic History-Matching Framework With Sparse Representation Based On Wavelet Multiresolution Analysis

et al. 2016

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In this work we propose an ensemble 4D seismic history matching framework for reservoir characterization. Compared to similar existing frameworks in reservoir engineering community, the proposed one consists of some relatively new ingredients, in terms of the type of seismic data in choice, wavelet multiresolution analysis for the chosen seismic data and related data noise estimation, and the use of recently developed iterative ensemble history matching algorithms.Typical seismic data used for history matching, such as acoustic impedance, are inverted quantities, whereas extra uncertainties may arise during the inversion processes. In the proposed framework we avoid such intermediate inversion processes. In addition, we also adopt wavelet-based sparse representation to reduce data size. Concretely, we use intercept and gradient attributes derived from amplitude versus angle (AVA) data, apply multilevel discrete wavelet transforms (DWT) to attribute data, and estimate noise level of resulting wavelet coefficients. We then select the wavelet coefficients above a certain threshold value, and history-match these leading wavelet coefficients using an iterative ensemble smoother.As a proof-of-concept study, we apply the proposed framework to a 2D synthetic case originated from a 3D Norne field model. The reservoir model variables to be estimated are permeability (PERMX) and porosity (PORO) at each active gridblock. A rock physics model is used to calculate seismic parameters (velocity and density) from reservoir properties (porosity, fluid saturation and pressure), then reflection coefficients are generated using a linearized AVA equation that involves velocity and density. AVA data are obtained by computing the convolution between reflection coefficients and a Ricker wavelet function. The multiresolution analysis applied to the AVA attributes helps to obtain a good estimation of 1 noise level and substantially reduce the data size. We compare history matching performance in three scenarios: (S1) with production data only, (S2) with seismic data only, and (S3) with both production and seismic data. In either scenario S2 or S3, we also inspect two sets of experiments, one using the original seismic data (full-data experiment) and the other adopting sparse representations (sparse-data experiment). Our numerical results suggest that, in this particular case study, using production data largely improves the estimation of permeability, but has little effect on the estimation of porosity. Using seismic data only improves the estimation of porosity, but not that of permeability. In contrast, using both production and 4D seismic data improves the estimation accuracies of both porosity and permeability. Moreover, in either scenario S2 or S3, provided that a certain stopping criterion is equipped in the iterative ensemble smoother, adopting sparse representations results in better history matching performance than using the original data set.

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Improvement of Levenberg-Marquardt algorithm during history fitting for reservoir simulation

Zhang¹,

Awotunde

2016

Petroleum Exploration and Development

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A multiresolution adjoint sensitivity analysis of time-lapse saturation maps

Cited by 7 publications

References 29 publications

Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering

Efficient big data assimilation through sparse representation: A 3D benchmark case study in petroleum engineering

An Ensemble 4D-Seismic History-Matching Framework With Sparse Representation Based On Wavelet Multiresolution Analysis

Improvement of Levenberg-Marquardt algorithm during history fitting for reservoir simulation

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